query: The query consists of users’ needs, leading to several research questions that span a paragraph. Each candidate passage is an abstract from a scientific paper. The objective of this information retrieval task is to identify the abstract that most effectively meets the user’s needs in the query : I aim to create a chatbot capable of interpreting images as input and analyzing them for subsequent tasks. The model should deliver responses efficiently without compromising the quality of the answers. To achieve this, I require an image analysis model that can swiftly perform image classification by ignoring unnecessary local detailed features. Additionally, the model should be capable of executing pixel-level localization and analysis as required. To enhance efficiency, I plan to incorporate reinforcement learning for user prediction. I envision the chatbot predicting the user's potential next questions, allowing the image analysis model to analyze relevant images while awaiting user input. The reward component of the framework should be directly linked to the accuracy of the prediction and the time saved. Furthermore, I want the method to remember the most frequently asked questions related to popular images, enabling it to provide immediate answers to these queries.

negative:
The accuracy of deep convolutional neural networks (CNNs) generally improves
when fueled with high resolution images. However, this often comes at a high
computational cost and high memory footprint. Inspired by the fact that not all
regions in an image are task-relevant, we propose a novel framework that
performs efficient image classification by processing a sequence of relatively
small inputs, which are strategically selected from the original image with
reinforcement learning. Such a dynamic decision process naturally facilitates
adaptive inference at test time, i.e., it can be terminated once the model is
sufficiently confident about its prediction and thus avoids further redundant
computation. Notably, our framework is general and flexible as it is compatible
with most of the state-of-the-art light-weighted CNNs (such as MobileNets,
EfficientNets and RegNets), which can be conveniently deployed as the backbone
feature extractor. Experiments on ImageNet show that our method consistently
improves the computational efficiency of a wide variety of deep models. For
example, it further reduces the average latency of the highly efficient
MobileNet-V3 on an iPhone XS Max by 20% without sacrificing accuracy. Code and
pre-trained models are available at
https://github.com/blackfeather-wang/GFNet-Pytorch.

negative:
Document image classification remains a popular research area because it can
be commercialized in many enterprise applications across different industries.
Recent advancements in large pre-trained computer vision and language models
and graph neural networks has lent document image classification many tools.
However using large pre-trained models usually requires substantial computing
resources which could defeat the cost-saving advantages of automatic document
image classification. In the paper we propose an efficient document image
classification framework that uses graph convolution neural networks and
incorporates textual, visual and layout information of the document. We have
rigorously benchmarked our proposed algorithm against several state-of-art
vision and language models on both publicly available dataset and a real-life
insurance document classification dataset. Empirical results on both publicly
available and real-world data show that our methods achieve near SOTA
performance yet require much less computing resources and time for model
training and inference. This results in solutions than offer better cost
advantages, especially in scalable deployment for enterprise applications. The
results showed that our algorithm can achieve classification performance quite
close to SOTA. We also provide comprehensive comparisons of computing
resources, model sizes, train and inference time between our proposed methods
and baselines. In addition we delineate the cost per image using our method and
other baselines.

negative:
As a training and analysis strategy for convolutional neural networks (CNNs),
we slice images into tiled segments and use, for training and prediction,
segments that both satisfy a criterion of information diversity and contain
sufficient content to support classification. In particular, we utilize image
entropy as the diversity criterion. This ensures that each tile carries as much
information diversity as the original image, and for many applications serves
as an indicator of usefulness in classification. To make predictions, a
probability aggregation framework is applied to probabilities assigned by the
CNN to the input image tiles. This technique facilitates the use of large,
high-resolution images that would be impractical to analyze unmodified;
provides data augmentation for training, which is particularly valuable when
image availability is limited; and the ensemble nature of the input for
prediction enhances its accuracy.

negative:
Automatically generating maps from satellite images is an important task.
There is a body of literature which tries to address this challenge. We created
a more expansive survey of the task by experimenting with different models and
adding new loss functions to improve results. We created a database of pairs of
satellite images and the corresponding map of the area. Our model translates
the satellite image to the corresponding standard layer map image using three
main model architectures: (i) a conditional Generative Adversarial Network
(GAN) which compresses the images down to a learned embedding, (ii) a generator
which is trained as a normalizing flow (RealNVP) model, and (iii) a conditional
GAN where the generator translates via a series of convolutions to the standard
layer of a map and the discriminator input is the concatenation of the
real/generated map and the satellite image. Model (iii) was by far the most
promising of three models. To improve the results we also added a
reconstruction loss and style transfer loss in addition to the GAN losses. The
third model architecture produced the best quality of sampled images. In
contrast to the other generative model where evaluation of the model is a
challenging problem. since we have access to the real map for a given satellite
image, we are able to assign a quantitative metric to the quality of the
generated images in addition to inspecting them visually. While we are
continuing to work on increasing the accuracy of the model, one challenge has
been the coarse resolution of the data which upper-bounds the quality of the
results of our model. Nevertheless, as will be seen in the results, the
generated map is more accurate in the features it produces since the generator
architecture demands a pixel-wise image translation/pixel-wise coloring. A
video presentation summarizing this paper is available at:
https://youtu.be/Ur0flOX-Ji0

negative:
Band selection refers to the process of choosing the most relevant bands in a
hyperspectral image. By selecting a limited number of optimal bands, we aim at
speeding up model training, improving accuracy, or both. It reduces redundancy
among spectral bands while trying to preserve the original information of the
image. By now many efforts have been made to develop unsupervised band
selection approaches, of which the majority are heuristic algorithms devised by
trial and error. In this paper, we are interested in training an intelligent
agent that, given a hyperspectral image, is capable of automatically learning
policy to select an optimal band subset without any hand-engineered reasoning.
To this end, we frame the problem of unsupervised band selection as a Markov
decision process, propose an effective method to parameterize it, and finally
solve the problem by deep reinforcement learning. Once the agent is trained, it
learns a band-selection policy that guides the agent to sequentially select
bands by fully exploiting the hyperspectral image and previously picked bands.
Furthermore, we propose two different reward schemes for the environment
simulation of deep reinforcement learning and compare them in experiments.
This, to the best of our knowledge, is the first study that explores a deep
reinforcement learning model for hyperspectral image analysis, thus opening a
new door for future research and showcasing the great potential of deep
reinforcement learning in remote sensing applications. Extensive experiments
are carried out on four hyperspectral data sets, and experimental results
demonstrate the effectiveness of the proposed method.

negative:
Convolutional Neural Networks (CNNs) are state-of-the-art models for document
image classification tasks. However, many of these approaches rely on
parameters and architectures designed for classifying natural images, which
differ from document images. We question whether this is appropriate and
conduct a large empirical study to find what aspects of CNNs most affect
performance on document images. Among other results, we exceed the
state-of-the-art on the RVL-CDIP dataset by using shear transform data
augmentation and an architecture designed for a larger input image.
Additionally, we analyze the learned features and find evidence that CNNs
trained on RVL-CDIP learn region-specific layout features.

negative:
In this paper we present a data-driven approach to obtain the static image of
a scene, eliminating dynamic objects that might have been present at the time
of traversing the scene with a camera. The general objective is to improve
vision-based localization and mapping tasks in dynamic environments, where the
presence (or absence) of different dynamic objects in different moments makes
these tasks less robust. We introduce an end-to-end deep learning framework to
turn images of an urban environment that include dynamic content, such as
vehicles or pedestrians, into realistic static frames suitable for localization
and mapping. This objective faces two main challenges: detecting the dynamic
objects, and inpainting the static occluded back-ground. The first challenge is
addressed by the use of a convolutional network that learns a multi-class
semantic segmentation of the image. The second challenge is approached with a
generative adversarial model that, taking as input the original dynamic image
and the computed dynamic/static binary mask, is capable of generating the final
static image. This framework makes use of two new losses, one based on image
steganalysis techniques, useful to improve the inpainting quality, and another
one based on ORB features, designed to enhance feature matching between real
and hallucinated image regions. To validate our approach, we perform an
extensive evaluation on different tasks that are affected by dynamic entities,
i.e., visual odometry, place recognition and multi-view stereo, with the
hallucinated images. Code has been made available on
https://github.com/bertabescos/EmptyCities_SLAM.

negative:
Image classification is the task of assigning to an input image a label from
a fixed set of categories. One of its most important applicative fields is that
of robotics, in particular the needing of a robot to be aware of what's around
and the consequent exploitation of that information as a benefit for its tasks.
In this work we consider the problem of a robot that enters a new environment
and wants to understand visual data coming from its camera, so to extract
knowledge from them. As main novelty we want to overcome the needing of a
physical robot, as it could be expensive and unhandy, so to hopefully enhance,
speed up and ease the research in this field. That's why we propose to develop
an application for a mobile platform that wraps several deep visual recognition
tasks. First we deal with a simple Image classification, testing a model
obtained from an AlexNet trained on the ILSVRC 2012 dataset. Several photo
settings are considered to better understand which factors affect most the
quality of classification. For the same purpose we are interested to integrate
the classification task with an extra module dealing with segmentation of the
object inside the image. In particular we propose a technique for extracting
the object shape and moving out all the background, so to focus the
classification only on the region occupied by the object. Another significant
task that is included is that of object discovery. Its purpose is to simulate
the situation in which the robot needs a certain object to complete one of its
activities. It starts searching for what it needs by looking around and trying
to understand the location of the object by scanning the surrounding
environment. Finally we provide a tool for dealing with the creation of
customized task-specific databases, meant to better suit to one's needing in a
particular vision task.

negative:
Automatically generating a natural language description of an image is a task
close to the heart of image understanding. In this paper, we present a
multi-model neural network method closely related to the human visual system
that automatically learns to describe the content of images. Our model consists
of two sub-models: an object detection and localization model, which extract
the information of objects and their spatial relationship in images
respectively; Besides, a deep recurrent neural network (RNN) based on long
short-term memory (LSTM) units with attention mechanism for sentences
generation. Each word of the description will be automatically aligned to
different objects of the input image when it is generated. This is similar to
the attention mechanism of the human visual system. Experimental results on the
COCO dataset showcase the merit of the proposed method, which outperforms
previous benchmark models.

negative:
Grounding human-machine conversation in a document is an effective way to
improve the performance of retrieval-based chatbots. However, only a part of
the document content may be relevant to help select the appropriate response at
a round. It is thus crucial to select the part of document content relevant to
the current conversation context. In this paper, we propose a document content
selection network (CSN) to perform explicit selection of relevant document
contents, and filter out the irrelevant parts. We show in experiments on two
public document-grounded conversation datasets that CSN can effectively help
select the relevant document contents to the conversation context, and it
produces better results than the state-of-the-art approaches. Our code and
datasets are available at https://github.com/DaoD/CSN.

negative:
Document classification is a challenging task with important applications.
The deep learning approaches to the problem have gained much attention
recently. Despite the progress, the proposed models do not incorporate the
knowledge of the document structure in the architecture efficiently and not
take into account the contexting importance of words and sentences. In this
paper, we propose a new approach based on a combination of convolutional neural
networks, gated recurrent units, and attention mechanisms for document
classification tasks. The main contribution of this work is the use of
convolution layers to extract more meaningful, generalizable and abstract
features by the hierarchical representation. The proposed method in this paper
improves the results of the current attention-based approaches for document
classification.

negative:
Document image enhancement and binarization methods are often used to improve
the accuracy and efficiency of document image analysis tasks such as text
recognition. Traditional non-machine-learning methods are constructed on
low-level features in an unsupervised manner but have difficulty with
binarization on documents with severely degraded backgrounds. Convolutional
neural network-based methods focus only on grayscale images and on local
textual features. In this paper, we propose a two-stage color document image
enhancement and binarization method using generative adversarial neural
networks. In the first stage, four color-independent adversarial networks are
trained to extract color foreground information from an input image for
document image enhancement. In the second stage, two independent adversarial
networks with global and local features are trained for image binarization of
documents of variable size. For the adversarial neural networks, we formulate
loss functions between a discriminator and generators having an encoder-decoder
structure. Experimental results show that the proposed method achieves better
performance than many classical and state-of-the-art algorithms over the
Document Image Binarization Contest (DIBCO) datasets, the LRDE Document
Binarization Dataset (LRDE DBD), and our shipping label image dataset. We plan
to release the shipping label dataset as well as our implementation code at
github.com/opensuh/DocumentBinarization/.

negative:
Graphical elements: particularly tables and figures contain a visual summary
of the most valuable information contained in a document. Therefore,
localization of such graphical objects in the document images is the initial
step to understand the content of such graphical objects or document images. In
this paper, we present a novel end-to-end trainable deep learning based
framework to localize graphical objects in the document images called as
Graphical Object Detection (GOD). Our framework is data-driven and does not
require any heuristics or meta-data to locate graphical objects in the document
images. The GOD explores the concept of transfer learning and domain adaptation
to handle scarcity of labeled training images for graphical object detection
task in the document images. Performance analysis carried out on the various
public benchmark data sets: ICDAR-2013, ICDAR-POD2017,and UNLV shows that our
model yields promising results as compared to state-of-the-art techniques.

negative:
We propose an end-to-end image compression and analysis model with
Transformers, targeting to the cloud-based image classification application.
Instead of placing an existing Transformer-based image classification model
directly after an image codec, we aim to redesign the Vision Transformer (ViT)
model to perform image classification from the compressed features and
facilitate image compression with the long-term information from the
Transformer. Specifically, we first replace the patchify stem (i.e., image
splitting and embedding) of the ViT model with a lightweight image encoder
modelled by a convolutional neural network. The compressed features generated
by the image encoder are injected convolutional inductive bias and are fed to
the Transformer for image classification bypassing image reconstruction.
Meanwhile, we propose a feature aggregation module to fuse the compressed
features with the selected intermediate features of the Transformer, and feed
the aggregated features to a deconvolutional neural network for image
reconstruction. The aggregated features can obtain the long-term information
from the self-attention mechanism of the Transformer and improve the
compression performance. The rate-distortion-accuracy optimization problem is
finally solved by a two-step training strategy. Experimental results
demonstrate the effectiveness of the proposed model in both the image
compression and the classification tasks.

negative:
When digitizing a document into an image, it is common to include a
surrounding border region to visually indicate that the entire document is
present in the image. However, this border should be removed prior to automated
processing. In this work, we present a deep learning based system, PageNet,
which identifies the main page region in an image in order to segment content
from both textual and non-textual border noise. In PageNet, a Fully
Convolutional Network obtains a pixel-wise segmentation which is post-processed
into the output quadrilateral region. We evaluate PageNet on 4 collections of
historical handwritten documents and obtain over 94% mean intersection over
union on all datasets and approach human performance on 2 of these collections.
Additionally, we show that PageNet can segment documents that are overlayed on
top of other documents.

negative:
Recent research advances in Computer Vision and Natural Language Processing
have introduced novel tasks that are paving the way for solving AI-complete
problems. One of those tasks is called Visual Question Answering (VQA). A VQA
system must take an image and a free-form, open-ended natural language question
about the image, and produce a natural language answer as the output. Such a
task has drawn great attention from the scientific community, which generated a
plethora of approaches that aim to improve the VQA predictive accuracy. Most of
them comprise three major components: (i) independent representation learning
of images and questions; (ii) feature fusion so the model can use information
from both sources to answer visual questions; and (iii) the generation of the
correct answer in natural language. With so many approaches being recently
introduced, it became unclear the real contribution of each component for the
ultimate performance of the model. The main goal of this paper is to provide a
comprehensive analysis regarding the impact of each component in VQA models.
Our extensive set of experiments cover both visual and textual elements, as
well as the combination of these representations in form of fusion and
attention mechanisms. Our major contribution is to identify core components for
training VQA models so as to maximize their predictive performance.

negative:
Rapid increase of digitized document give birth to high demand of document
image retrieval. While conventional document image retrieval approaches depend
on complex OCR-based text recognition and text similarity detection, this paper
proposes a new content-based approach, in which more attention is paid to
features extraction and fusion. In the proposed approach, multiple features of
document images are extracted by different CNN models. After that, the
extracted CNN features are reduced and fused into weighted average feature.
Finally, the document images are ranked based on feature similarity to a
provided query image. Experimental procedure is performed on a group of
document images that transformed from academic papers, which contain both
English and Chinese document, the results show that the proposed approach has
good ability to retrieve document images with similar text content, and the
fusion of CNN features can effectively improve the retrieval accuracy.

negative:
The task of Visual Commonsense Reasoning is extremely challenging in the
sense that the model has to not only be able to answer a question given an
image, but also be able to learn to reason. The baselines introduced in this
task are quite limiting because two networks are trained for predicting answers
and rationales separately. Question and image is used as input to train answer
prediction network while question, image and correct answer are used as input
in the rationale prediction network. As rationale is conditioned on the correct
answer, it is based on the assumption that we can solve Visual Question
Answering task without any error - which is over ambitious. Moreover, such an
approach makes both answer and rationale prediction two completely independent
VQA tasks rendering cognition task meaningless. In this paper, we seek to
address these issues by proposing an end-to-end trainable model which considers
both answers and their reasons jointly. Specifically, we first predict the
answer for the question and then use the chosen answer to predict the
rationale. However, a trivial design of such a model becomes non-differentiable
which makes it difficult to train. We solve this issue by proposing four
approaches - softmax, gumbel-softmax, reinforcement learning based sampling and
direct cross entropy against all pairs of answers and rationales. We
demonstrate through experiments that our model performs competitively against
current state-of-the-art. We conclude with an analysis of presented approaches
and discuss avenues for further work.

negative:
A conversational agent (chatbot) is a piece of software that is able to
communicate with humans using natural language. Modeling conversation is an
important task in natural language processing and artificial intelligence.
While chatbots can be used for various tasks, in general they have to
understand users' utterances and provide responses that are relevant to the
problem at hand.
  In my work, I conduct an in-depth survey of recent literature, examining over
70 publications related to chatbots published in the last 3 years. Then, I
proceed to make the argument that the very nature of the general conversation
domain demands approaches that are different from current state-of-of-the-art
architectures. Based on several examples from the literature I show why current
chatbot models fail to take into account enough priors when generating
responses and how this affects the quality of the conversation. In the case of
chatbots, these priors can be outside sources of information that the
conversation is conditioned on like the persona or mood of the conversers. In
addition to presenting the reasons behind this problem, I propose several ideas
on how it could be remedied.
  The next section focuses on adapting the very recent Transformer model to the
chatbot domain, which is currently state-of-the-art in neural machine
translation. I first present experiments with the vanilla model, using
conversations extracted from the Cornell Movie-Dialog Corpus. Secondly, I
augment the model with some of my ideas regarding the issues of encoder-decoder
architectures. More specifically, I feed additional features into the model
like mood or persona together with the raw conversation data. Finally, I
conduct a detailed analysis of how the vanilla model performs on conversational
data by comparing it to previous chatbot models and how the additional features
affect the quality of the generated responses.

negative:
We propose Pixel-BERT to align image pixels with text by deep multi-modal
transformers that jointly learn visual and language embedding in a unified
end-to-end framework. We aim to build a more accurate and thorough connection
between image pixels and language semantics directly from image and sentence
pairs instead of using region-based image features as the most recent vision
and language tasks. Our Pixel-BERT which aligns semantic connection in pixel
and text level solves the limitation of task-specific visual representation for
vision and language tasks. It also relieves the cost of bounding box
annotations and overcomes the unbalance between semantic labels in visual task
and language semantic. To provide a better representation for down-stream
tasks, we pre-train a universal end-to-end model with image and sentence pairs
from Visual Genome dataset and MS-COCO dataset. We propose to use a random
pixel sampling mechanism to enhance the robustness of visual representation and
to apply the Masked Language Model and Image-Text Matching as pre-training
tasks. Extensive experiments on downstream tasks with our pre-trained model
show that our approach makes the most state-of-the-arts in downstream tasks,
including Visual Question Answering (VQA), image-text retrieval, Natural
Language for Visual Reasoning for Real (NLVR). Particularly, we boost the
performance of a single model in VQA task by 2.17 points compared with SOTA
under fair comparison.

negative:
Current conversational systems can follow simple commands and answer basic
questions, but they have difficulty maintaining coherent and open-ended
conversations about specific topics. Competitions like the Conversational
Intelligence (ConvAI) challenge are being organized to push the research
development towards that goal. This article presents in detail the RLLChatbot
that participated in the 2017 ConvAI challenge. The goal of this research is to
better understand how current deep learning and reinforcement learning tools
can be used to build a robust yet flexible open domain conversational agent. We
provide a thorough description of how a dialog system can be built and trained
from mostly public-domain datasets using an ensemble model. The first
contribution of this work is a detailed description and analysis of different
text generation models in addition to novel message ranking and selection
methods. Moreover, a new open-source conversational dataset is presented.
Training on this data significantly improves the Recall@k score of the ranking
and selection mechanisms compared to our baseline model responsible for
selecting the message returned at each interaction.

negative:
This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive representations. The current work explores this capacity in the
realm of document analysis, and confirms that this representation strategy is
superior to a variety of popular hand-crafted alternatives. Experiments also
show that (i) features extracted from CNNs are robust to compression, (ii) CNNs
trained on non-document images transfer well to document analysis tasks, and
(iii) enforcing region-specific feature-learning is unnecessary given
sufficient training data. This work also makes available a new labelled subset
of the IIT-CDIP collection, containing 400,000 document images across 16
categories, useful for training new CNNs for document analysis.

negative:
The demand for on-device document recognition systems increases in
conjunction with the emergence of more strict privacy and security
requirements. In such systems, there is no data transfer from the end device to
a third-party information processing servers. The response time is vital to the
user experience of on-device document recognition. Combined with the
unavailability of discrete GPUs, powerful CPUs, or a large RAM capacity on
consumer-grade end devices such as smartphones, the time limitations put
significant constraints on the computational complexity of the applied
algorithms for on-device execution.
  In this work, we consider document location in an image without prior
knowledge of the document content or its internal structure. In accordance with
the published works, at least 5 systems offer solutions for on-device document
location. All these systems use a location method which can be considered
Hough-based. The precision of such systems seems to be lower than that of the
state-of-the-art solutions which were not designed to account for the limited
computational resources.
  We propose an advanced Hough-based method. In contrast with other approaches,
it accounts for the geometric invariants of the central projection model and
combines both edge and color features for document boundary detection. The
proposed method allowed for the second best result for SmartDoc dataset in
terms of precision, surpassed by U-net like neural network. When evaluated on a
more challenging MIDV-500 dataset, the proposed algorithm guaranteed the best
precision compared to published methods. Our method retained the applicability
to on-device computations.

negative:
We consider the problem of accurately and efficiently querying a remote
server to retrieve information about images captured by a mobile device. In
addition to reduced transmission overhead and computational complexity, the
retrieval protocol should be robust to variations in the image acquisition
process, such as translation, rotation, scaling, and sensor-related
differences. We propose to extract scale-invariant image features and then
perform clustering to reduce the number of features needed for image matching.
Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF)
are investigated as candidate clustering approaches. The image matching
complexity at the database server is quadratic in the (small) number of
clusters, not in the (very large) number of image features. We employ an
image-dependent information content metric to approximate the model order,
i.e., the number of clusters, needed for accurate matching, which is preferable
to setting the model order using trial and error. We show how to combine the
hypotheses provided by PCA and NMF factor loadings, thereby obtaining more
accurate retrieval than using either approach alone. In experiments on a
database of urban images, we obtain a top-1 retrieval accuracy of 89% and a
top-3 accuracy of 92.5%.

negative:
In this paper, we focus on the problem of unsupervised image-sentence
matching. Existing research explores to utilize document-level structural
information to sample positive and negative instances for model training.
Although the approach achieves positive results, it introduces a sampling bias
and fails to distinguish instances with high semantic similarity. To alleviate
the bias, we propose a new sampling strategy to select additional
intra-document image-sentence pairs as positive or negative samples.
Furthermore, to recognize the complex pattern in intra-document samples, we
propose a Transformer based model to capture fine-grained features and
implicitly construct a graph for each document, where concepts in a document
are introduced to bridge the representation learning of images and sentences in
the context of a document. Experimental results show the effectiveness of our
approach to alleviate the bias and learn well-aligned multimodal
representations.

negative:
With the rising use of Machine Learning (ML) and Deep Learning (DL) in
various industries, the medical industry is also not far behind. A very simple
yet extremely important use case of ML in this industry is for image
classification. This is important for doctors to help them detect certain
diseases timely, thereby acting as an aid to reduce chances of human judgement
error. However, when using automated systems like these, there is a privacy
concern as well. Attackers should not be able to get access to the medical
records and images of the patients. It is also required that the model be
secure, and that the data that is sent to the model and the predictions that
are received both should not be revealed to the model in clear text.
  In this study, we aim to solve these problems in the context of a medical
image classification problem of detection of pneumonia by examining chest x-ray
images.

negative:
Vision based localization is the problem of inferring the pose of the camera
given a single image. One solution to this problem is to learn a deep neural
network to infer the pose of a query image after learning on a dataset of
images with known poses. Another more commonly used approach rely on image
retrieval where the query image is compared against the database of images and
its pose is inferred with the help of the retrieved images. The latter approach
assumes that images taken from the same places consists of the same landmarks
and, thus would have similar feature representations. These representation can
be learned using full supervision to be robust to different variations in
capture conditions like time of the day and weather. In this work, we introduce
a framework to enhance the performance of these retrieval based localization
methods by taking into account the additional information including GPS
coordinates and temporal neighbourhood of the images provided by the
acquisition process in addition to the descriptor similarity of pairs of images
in the reference or query database which is used traditionally for
localization. Our method constructs a graph based on this additional
information and use it for robust retrieval by smoothing the feature
representation of reference and/or query images. We show that the proposed
method is able to significantly improve the localization accuracy on two large
scale datasets over the baselines.

negative:
Errors in medication intake among elderly people are very common. One of the
main causes for this is their loss of ability to retain information. The high
amount of medicine intake required by the advanced age is another limiting
factor. Thence, the design of an interactive aid system, preferably using
natural language, to help the older population with medication is in demand. A
chatbot based on a subtractive cluster algorithm, included in unsupervised
learned models, is the chosen solution since the processing of natural
languages is a necessary step in view to construct a chatbot able to answer
questions that older people may pose upon themselves concerning a particular
drug. In this work, the subtractive mountain clustering algorithm has been
adapted to the problem of natural languages processing. This algorithm version
allows for the association of a set of words into clusters. After finding the
centre of every cluster -- the most relevant word, all the others are
aggregated according to a defined metric adapted to the language processing
realm. All the relevant stored information is processed, as well as the
questions, by the algorithm. The correct processing of the text enables the
chatbot to produce answers that relate to the posed queries. To validate the
method, we use the package insert of a drug as the available information and
formulate associated questions.

negative:
Conventional methods for the image-text generation tasks mainly tackle the
naturally bidirectional generation tasks separately, focusing on designing
task-specific frameworks to improve the quality and fidelity of the generated
samples. Recently, Vision-Language Pre-training models have greatly improved
the performance of the image-to-text generation tasks, but large-scale
pre-training models for text-to-image synthesis task are still under-developed.
In this paper, we propose ERNIE-ViLG, a unified generative pre-training
framework for bidirectional image-text generation with transformer model. Based
on the image quantization models, we formulate both image generation and text
generation as autoregressive generative tasks conditioned on the text/image
input. The bidirectional image-text generative modeling eases the semantic
alignments across vision and language. For the text-to-image generation
process, we further propose an end-to-end training method to jointly learn the
visual sequence generator and the image reconstructor. To explore the landscape
of large-scale pre-training for bidirectional text-image generation, we train a
10-billion parameter ERNIE-ViLG model on a large-scale dataset of 145 million
(Chinese) image-text pairs which achieves state-of-the-art performance for both
text-to-image and image-to-text tasks, obtaining an FID of 7.9 on MS-COCO for
text-to-image synthesis and best results on COCO-CN and AIC-ICC for image
captioning.

negative:
The ability to engage in goal-oriented conversations has allowed humans to
gain knowledge, reduce uncertainty, and perform tasks more efficiently.
Artificial agents, however, are still far behind humans in having goal-driven
conversations. In this work, we focus on the task of goal-oriented visual
dialogue, aiming to automatically generate a series of questions about an image
with a single objective. This task is challenging since these questions must
not only be consistent with a strategy to achieve a goal, but also consider the
contextual information in the image. We propose an end-to-end goal-oriented
visual dialogue system, that combines reinforcement learning with regularized
information gain. Unlike previous approaches that have been proposed for the
task, our work is motivated by the Rational Speech Act framework, which models
the process of human inquiry to reach a goal. We test the two versions of our
model on the GuessWhat?! dataset, obtaining significant results that outperform
the current state-of-the-art models in the task of generating questions to find
an undisclosed object in an image.

negative:
Most popular goal-oriented dialogue agents are capable of understanding the
conversational context. However, with the surge of virtual assistants with
screen, the next generation of agents are required to also understand screen
context in order to provide a proper interactive experience, and better
understand users' goals. In this paper, we propose a novel multimodal
conversational framework, where the dialogue agent's next action and their
arguments are derived jointly conditioned both on the conversational and the
visual context. Specifically, we propose a new model, that can reason over the
visual context within a conversation and populate API arguments with visual
entities given the user query. Our model can recognize visual features such as
color and shape as well as the metadata based features such as price or star
rating associated with a visual entity. In order to train our model, due to a
lack of suitable multimodal conversational datasets, we also propose a novel
multimodal dialog simulator to generate synthetic data and also collect
realistic user data from MTurk to improve model robustness. The proposed model
achieves a reasonable 85% model accuracy, without high inference latency. We
also demonstrate the proposed approach in a prototypical furniture shopping
experience for a multimodal virtual assistant.

negative:
Cloud computing offers the potential to help scientists to process massive
number of computing resources often required in machine learning application
such as computer vision problems. This proposal would like to show that which
benefits can be obtained from cloud in order to help medical image analysis
users (including scientists, clinicians, and research institutes). As security
and privacy of algorithms are important for most of algorithms inventors, these
algorithms can be hidden in a cloud to allow the users to use the algorithms as
a package without any access to see/change their inside. In another word, in
the user part, users send their images to the cloud and configure the algorithm
via an interface. In the cloud part, the algorithms are applied to this image
and the results are returned back to the user. My proposal has two parts: (1)
investigate the potential of cloud computing for computer vision problems and
(2) study the components of a proposed cloud-based framework for medical image
analysis application and develop them (depending on the length of the
internship). The investigation part will involve a study on several aspects of
the problem including security, usability (for medical end users of the
service), appropriate programming abstractions for vision problems, scalability
and resource requirements. In the second part of this proposal I am going to
thoroughly study of the proposed framework components and their relations and
develop them. The proposed cloud-based framework includes an integrated
environment to enable scientists and clinicians to access to the previous and
current medical image analysis algorithms using a handful user interface
without any access to the algorithm codes and procedures.

negative:
Recently, the progress of learning-by-synthesis has proposed a training model
for synthetic images, which can effectively reduce the cost of human and
material resources. However, due to the different distribution of synthetic
images compared to real images, the desired performance cannot still be
achieved. Real images consist of multiple forms of light orientation, while
synthetic images consist of a uniform light orientation. These features are
considered to be characteristic of outdoor and indoor scenes, respectively. To
solve this problem, the previous method learned a model to improve the realism
of the synthetic image. Different from the previous methods, this paper takes
the first step to purify real images. Through the style transfer task, the
distribution of outdoor real images is converted into indoor synthetic images,
thereby reducing the influence of light. Therefore, this paper proposes a
real-time style transfer network that preserves image content information (eg,
gaze direction, pupil center position) of an input image (real image) while
inferring style information (eg, image color structure, semantic features) of
style image (synthetic image). In addition, the network accelerates the
convergence speed of the model and adapts to multi-scale images. Experiments
were performed using mixed studies (qualitative and quantitative) methods to
demonstrate the possibility of purifying real images in complex directions.
Qualitatively, it compares the proposed method with the available methods in a
series of indoor and outdoor scenarios of the LPW dataset. In quantitative
terms, it evaluates the purified image by training a gaze estimation model on
the cross data set. The results show a significant improvement over the
baseline method compared to the raw real image.

negative:
Document Grounded Conversations is a task to generate dialogue responses when
chatting about the content of a given document. Obviously, document knowledge
plays a critical role in Document Grounded Conversations, while existing
dialogue models do not exploit this kind of knowledge effectively enough. In
this paper, we propose a novel Transformer-based architecture for multi-turn
document grounded conversations. In particular, we devise an Incremental
Transformer to encode multi-turn utterances along with knowledge in related
documents. Motivated by the human cognitive process, we design a two-pass
decoder (Deliberation Decoder) to improve context coherence and knowledge
correctness. Our empirical study on a real-world Document Grounded Dataset
proves that responses generated by our model significantly outperform
competitive baselines on both context coherence and knowledge relevance.

negative:
How to leverage cross-document interactions to improve ranking performance is
an important topic in information retrieval (IR) research. However, this topic
has not been well-studied in the learning-to-rank setting and most of the
existing work still treats each document independently while scoring. The
recent development of deep learning shows strength in modeling complex
relationships across sequences and sets. It thus motivates us to study how to
leverage cross-document interactions for learning-to-rank in the deep learning
framework. In this paper, we formally define the permutation-equivariance
requirement for a scoring function that captures cross-document interactions.
We then propose a self-attention based document interaction network and show
that it satisfies the permutation-equivariant requirement, and can generate
scores for document sets of varying sizes. Our proposed methods can
automatically learn to capture document interactions without any auxiliary
information, and can scale across large document sets. We conduct experiments
on three ranking datasets: the benchmark Web30k, a Gmail search, and a Google
Drive Quick Access dataset. Experimental results show that our proposed methods
are both more effective and efficient than baselines.

negative:
Document Image Analysis, like any Digital Image Analysis requires
identification and extraction of proper features, which are generally extracted
from uncompressed images, though in reality images are made available in
compressed form for the reasons such as transmission and storage efficiency.
However, this implies that the compressed image should be decompressed, which
indents additional computing resources. This limitation induces the motivation
to research in extracting features directly from the compressed image. In this
research, we propose to extract essential features such as projection profile,
run-histogram and entropy for text document analysis directly from run-length
compressed text-documents. The experimentation illustrates that features are
extracted directly from the compressed image without going through the stage of
decompression, because of which the computing time is reduced. The feature
values so extracted are exactly identical to those extracted from uncompressed
images.

negative:
Although CNNs are widely considered as the state-of-the-art models in various
applications of image analysis, one of the main challenges still open is the
training of a CNN on high resolution images. Different strategies have been
proposed involving either a rescaling of the image or an individual processing
of parts of the image. Such strategies cannot be applied to images, such as
gigapixel histopathological images, for which a high reduction in resolution
inherently effects a loss of discriminative information, and in respect of
which the analysis of single parts of the image suffers from a lack of global
information or implies a high workload in terms of annotating the training
images in such a way as to select significant parts. We propose a method for
the analysis of gigapixel histopathological images solely by using weak
image-level labels. In particular, two analysis tasks are taken into account: a
binary classification and a prediction of the tumor proliferation score. Our
method is based on a CNN structure consisting of a compressing path and a
learning path. In the compressing path, the gigapixel image is packed into a
grid-based feature map by using a residual network devoted to the feature
extraction of each patch into which the image has been divided. In the learning
path, attention modules are applied to the grid-based feature map, taking into
account spatial correlations of neighboring patch features to find regions of
interest, which are then used for the final whole slide analysis. Our method
integrates both global and local information, is flexible with regard to the
size of the input images and only requires weak image-level labels. Comparisons
with different methods of the state-of-the-art on two well known datasets,
Camelyon16 and TUPAC16, have been made to confirm the validity of the proposed
model.

negative:
Weakly supervised learning of object detection is an important problem in
image understanding that still does not have a satisfactory solution. In this
paper, we address this problem by exploiting the power of deep convolutional
neural networks pre-trained on large-scale image-level classification tasks. We
propose a weakly supervised deep detection architecture that modifies one such
network to operate at the level of image regions, performing simultaneously
region selection and classification. Trained as an image classifier, the
architecture implicitly learns object detectors that are better than
alternative weakly supervised detection systems on the PASCAL VOC data. The
model, which is a simple and elegant end-to-end architecture, outperforms
standard data augmentation and fine-tuning techniques for the task of
image-level classification as well.

negative:
Deep learning methods have proven extremely effective at performing a variety
of medical image analysis tasks. With their potential use in clinical routine,
their lack of transparency has however been one of their few weak points,
raising concerns regarding their behavior and failure modes. While most
research to infer model behavior has focused on indirect strategies that
estimate prediction uncertainties and visualize model support in the input
image space, the ability to explicitly query a prediction model regarding its
image content offers a more direct way to determine the behavior of trained
models. To this end, we present a novel Visual Question Answering approach that
allows an image to be queried by means of a written question. Experiments on a
variety of medical and natural image datasets show that by fusing image and
question features in a novel way, the proposed approach achieves an equal or
higher accuracy compared to current methods.

negative:
Existing methods for interactive image retrieval have demonstrated the merit
of integrating user feedback, improving retrieval results. However, most
current systems rely on restricted forms of user feedback, such as binary
relevance responses, or feedback based on a fixed set of relative attributes,
which limits their impact. In this paper, we introduce a new approach to
interactive image search that enables users to provide feedback via natural
language, allowing for more natural and effective interaction. We formulate the
task of dialog-based interactive image retrieval as a reinforcement learning
problem, and reward the dialog system for improving the rank of the target
image during each dialog turn. To mitigate the cumbersome and costly process of
collecting human-machine conversations as the dialog system learns, we train
our system with a user simulator, which is itself trained to describe the
differences between target and candidate images. The efficacy of our approach
is demonstrated in a footwear retrieval application. Experiments on both
simulated and real-world data show that 1) our proposed learning framework
achieves better accuracy than other supervised and reinforcement learning
baselines and 2) user feedback based on natural language rather than
pre-specified attributes leads to more effective retrieval results, and a more
natural and expressive communication interface.

negative:
Recognition of document images have important applications in restoring old
and classical texts. The problem involves quality improvement before passing it
to a properly trained OCR to get accurate recognition of the text. The image
enhancement and quality improvement constitute important steps as subsequent
recognition depends upon the quality of the input image. There are scenarios
when high resolution images are not available and our experiments show that the
OCR accuracy reduces significantly with decrease in the spatial resolution of
document images. Thus the only option is to improve the resolution of such
document images. The goal is to construct a high resolution image, given a
single low resolution binary image, which constitutes the problem of single
image super-resolution. Most of the previous work in super-resolution deal with
natural images which have more information-content than the document images.
Here, we use Convolution Neural Network to learn the mapping between low and
the corresponding high resolution images. We experiment with different number
of layers, parameter settings and non-linear functions to build a fast
end-to-end framework for document image super-resolution. Our proposed model
shows a very good PSNR improvement of about 4 dB on 75 dpi Tamil images,
resulting in a 3 % improvement of word level accuracy by the OCR. It takes less
time than the recent sparse based natural image super-resolution technique,
making it useful for real-time document recognition applications.

negative:
The quality of a document is affected by various factors, including
grammaticality, readability, stylistics, and expertise depth, making the task
of document quality assessment a complex one. In this paper, we explore this
task in the context of assessing the quality of Wikipedia articles and academic
papers. Observing that the visual rendering of a document can capture implicit
quality indicators that are not present in the document text --- such as
images, font choices, and visual layout --- we propose a joint model that
combines the text content with a visual rendering of the document for document
quality assessment. Experimental results over two datasets reveal that textual
and visual features are complementary, achieving state-of-the-art results.

negative:
Document AI, or Document Intelligence, is a relatively new research topic
that refers to the techniques for automatically reading, understanding, and
analyzing business documents. It is an important research direction for natural
language processing and computer vision. In recent years, the popularity of
deep learning technology has greatly advanced the development of Document AI,
such as document layout analysis, visual information extraction, document
visual question answering, document image classification, etc. This paper
briefly reviews some of the representative models, tasks, and benchmark
datasets. Furthermore, we also introduce early-stage heuristic rule-based
document analysis, statistical machine learning algorithms, and deep learning
approaches especially pre-training methods. Finally, we look into future
directions for Document AI research.

negative:
Information gathering in a partially observable environment can be formulated
as a reinforcement learning (RL), problem where the reward depends on the
agent's uncertainty. For example, the reward can be the negative entropy of the
agent's belief over an unknown (or hidden) variable. Typically, the rewards of
an RL agent are defined as a function of the state-action pairs and not as a
function of the belief of the agent; this hinders the direct application of
deep RL methods for such tasks. This paper tackles the challenge of using
belief-based rewards for a deep RL agent, by offering a simple insight that
maximizing any convex function of the belief of the agent can be approximated
by instead maximizing a prediction reward: a reward based on prediction
accuracy. In particular, we derive the exact error between negative entropy and
the expected prediction reward. This insight provides theoretical motivation
for several fields using prediction rewards---namely visual attention, question
answering systems, and intrinsic motivation---and highlights their connection
to the usually distinct fields of active perception, active sensing, and sensor
placement. Based on this insight we present deep anticipatory networks (DANs),
which enables an agent to take actions to reduce its uncertainty without
performing explicit belief inference. We present two applications of DANs:
building a sensor selection system for tracking people in a shopping mall and
learning discrete models of attention on fashion MNIST and MNIST digit
classification.

negative:
We present FlipDial, a generative model for visual dialogue that
simultaneously plays the role of both participants in a visually-grounded
dialogue. Given context in the form of an image and an associated caption
summarising the contents of the image, FlipDial learns both to answer questions
and put forward questions, capable of generating entire sequences of dialogue
(question-answer pairs) which are diverse and relevant to the image. To do
this, FlipDial relies on a simple but surprisingly powerful idea: it uses
convolutional neural networks (CNNs) to encode entire dialogues directly,
implicitly capturing dialogue context, and conditional VAEs to learn the
generative model. FlipDial outperforms the state-of-the-art model in the
sequential answering task (one-way visual dialogue) on the VisDial dataset by 5
points in Mean Rank using the generated answers. We are the first to extend
this paradigm to full two-way visual dialogue, where our model is capable of
generating both questions and answers in sequence based on a visual input, for
which we propose a set of novel evaluation measures and metrics.

negative:
Recent work in open-domain conversational agents has demonstrated that
significant improvements in model engagingness and humanness metrics can be
achieved via massive scaling in both pre-training data and model size
(Adiwardana et al., 2020; Roller et al., 2020). However, if we want to build
agents with human-like abilities, we must expand beyond handling just text. A
particularly important topic is the ability to see images and communicate about
what is perceived. With the goal of engaging humans in multi-modal dialogue, we
investigate combining components from state-of-the-art open-domain dialogue
agents with those from state-of-the-art vision models. We study incorporating
different image fusion schemes and domain-adaptive pre-training and fine-tuning
strategies, and show that our best resulting model outperforms strong existing
models in multi-modal dialogue while simultaneously performing as well as its
predecessor (text-only) BlenderBot (Roller et al., 2020) in text-based
conversation. We additionally investigate and incorporate safety components in
our final model, and show that such efforts do not diminish model performance
with respect to engagingness metrics.

negative:
Recently we see a rising number of methods in the field of eXplainable
Artificial Intelligence. To our surprise, their development is driven by model
developers rather than a study of needs for human end users. The analysis of
needs, if done, takes the form of an A/B test rather than a study of open
questions. To answer the question "What would a human operator like to ask the
ML model?" we propose a conversational system explaining decisions of the
predictive model. In this experiment, we developed a chatbot called dr_ant to
talk about machine learning model trained to predict survival odds on Titanic.
People can talk with dr_ant about different aspects of the model to understand
the rationale behind its predictions. Having collected a corpus of 1000+
dialogues, we analyse the most common types of questions that users would like
to ask. To our knowledge, it is the first study which uses a conversational
system to collect the needs of human operators from the interactive and
iterative dialogue explorations of a predictive model.

negative:
A big part of achieving Artificial General Intelligence(AGI) is to build a
machine that can see and listen like humans. Much work has focused on designing
models for image classification, video classification, object detection, pose
estimation, speech recognition, etc., and has achieved significant progress in
recent years thanks to deep learning. However, understanding the world is not
enough. An AI agent also needs to know how to talk, especially how to
communicate with a human. While perception (vision, for example) is more common
across animal species, the use of complicated language is unique to humans and
is one of the most important aspects of intelligence.
  In this thesis, we focus on generating textual output given visual input. In
Chapter 3, we focus on generating the referring expression, a text description
for an object in the image so that a receiver can infer which object is being
described. We use a comprehension machine to directly guide the generated
referring expressions to be more discriminative. In Chapter 4, we introduce a
method that encourages discriminability in image caption generation. We show
that more discriminative captioning models generate more descriptive captions.
In Chapter 5, we study how training objectives and sampling methods affect the
models' ability to generate diverse captions. We find that a popular captioning
training strategy will be detrimental to the diversity of generated captions.
In Chapter 6, we propose a model that can control the length of generated
captions. By changing the desired length, one can influence the style and
descriptiveness of the captions. Finally, in Chapter 7, we rank/generate
informative image tags according to their information utility. The proposed
method better matches what humans think are the most important tags for the
images.

negative:
One obstacle to applying reinforcement learning algorithms to real-world
problems is the lack of suitable reward functions. Designing such reward
functions is difficult in part because the user only has an implicit
understanding of the task objective. This gives rise to the agent alignment
problem: how do we create agents that behave in accordance with the user's
intentions? We outline a high-level research direction to solve the agent
alignment problem centered around reward modeling: learning a reward function
from interaction with the user and optimizing the learned reward function with
reinforcement learning. We discuss the key challenges we expect to face when
scaling reward modeling to complex and general domains, concrete approaches to
mitigate these challenges, and ways to establish trust in the resulting agents.

negative:
Conventional seq2seq chatbot models attempt only to find sentences with the
highest probabilities conditioned on the input sequences, without considering
the sentiment of the output sentences. In this paper, we investigate four
models to scale or adjust the sentiment of the chatbot response: a
persona-based model, reinforcement learning, a plug and play model, and
CycleGAN, all based on the seq2seq model. We also develop machine-evaluated
metrics to estimate whether the responses are reasonable given the input. These
metrics, together with human evaluation, are used to analyze the performance of
the four models in terms of different aspects; reinforcement learning and
CycleGAN are shown to be very attractive.

negative:
Document-level machine translation focuses on the translation of entire
documents from a source to a target language. It is widely regarded as a
challenging task since the translation of the individual sentences in the
document needs to retain aspects of the discourse at document level. However,
document-level translation models are usually not trained to explicitly ensure
discourse quality. Therefore, in this paper we propose a training approach that
explicitly optimizes two established discourse metrics, lexical cohesion (LC)
and coherence (COH), by using a reinforcement learning objective. Experiments
over four different language pairs and three translation domains have shown
that our training approach has been able to achieve more cohesive and coherent
document translations than other competitive approaches, yet without
compromising the faithfulness to the reference translation. In the case of the
Zh-En language pair, our method has achieved an improvement of 2.46 percentage
points (pp) in LC and 1.17 pp in COH over the runner-up, while at the same time
improving 0.63 pp in BLEU score and 0.47 pp in F_BERT.

negative:
Using chatbots to deliver recommendations is increasingly popular. The design
of recommendation chatbots has primarily been taking an information-centric
approach by focusing on the recommended content per se. Limited attention is on
how social connection and relational strategies, such as self-disclosure from a
chatbot, may influence users' perception and acceptance of the recommendation.
In this work, we designed, implemented, and evaluated a social chatbot capable
of performing three different levels of self-disclosure: factual information
(low), cognitive opinions (medium), and emotions (high). In the evaluation, we
recruited 372 participants to converse with the chatbot on two topics: movies
and COVID-19 experiences. In each topic, the chatbot performed small talks and
made recommendations relevant to the topic. Participants were randomly assigned
to four experimental conditions where the chatbot used factual, cognitive,
emotional, and adaptive strategies to perform self-disclosures. By training a
text classifier to identify users' level of self-disclosure in real-time, the
adaptive chatbot can dynamically match its self-disclosure to the level of
disclosure exhibited by the users. Our results show that users reciprocate with
higher-level self-disclosure when a recommendation chatbot consistently
displays emotions throughout the conversation. Chatbot's emotional disclosure
also led to increased interactional enjoyment and more positive interpersonal
perception towards the bot, fostering a stronger human-chatbot relationship and
thus leading to increased recommendation effectiveness, including a higher
tendency to accept the recommendation. We discuss the understandings obtained
and implications to future design.

negative:
Under special circumstances, summaries should conform to a particular style
with patterns, such as court judgments and abstracts in academic papers. To
this end, the prototype document-summary pairs can be utilized to generate
better summaries. There are two main challenges in this task: (1) the model
needs to incorporate learned patterns from the prototype, but (2) should avoid
copying contents other than the patternized words---such as irrelevant
facts---into the generated summaries. To tackle these challenges, we design a
model named Prototype Editing based Summary Generator (PESG). PESG first learns
summary patterns and prototype facts by analyzing the correlation between a
prototype document and its summary. Prototype facts are then utilized to help
extract facts from the input document. Next, an editing generator generates new
summary based on the summary pattern or extracted facts. Finally, to address
the second challenge, a fact checker is used to estimate mutual information
between the input document and generated summary, providing an additional
signal for the generator. Extensive experiments conducted on a large-scale
real-world text summarization dataset show that PESG achieves the
state-of-the-art performance in terms of both automatic metrics and human
evaluations.

negative:
Since the low quality of document images will greatly undermine the chances
of success in automatic text recognition and analysis, it is necessary to
assess the quality of document images uploaded in online business process, so
as to reject those images of low quality. In this paper, we attempt to achieve
document image quality assessment and our contributions are twofold. Firstly,
since document image quality assessment is more interested in text, we propose
a text line based framework to estimate document image quality, which is
composed of three stages: text line detection, text line quality prediction,
and overall quality assessment. Text line detection aims to find potential text
lines with a detector. In the text line quality prediction stage, the quality
score is computed for each text line with a CNN-based prediction model. The
overall quality of document images is finally assessed with the ensemble of all
text line quality. Secondly, to train the prediction model, a large-scale
dataset, comprising 52,094 text line images, is synthesized with diverse
attributes. For each text line image, a quality label is computed with a
piece-wise function. To demonstrate the effectiveness of the proposed
framework, comprehensive experiments are evaluated on two popular document
image quality assessment benchmarks. Our framework significantly outperforms
the state-of-the-art methods by large margins on the large and complicated
dataset.

negative:
The number of social images has exploded by the wide adoption of social
networks, and people like to share their comments about them. These comments
can be a description of the image, or some objects, attributes, scenes in it,
which are normally used as the user-provided tags. However, it is well-known
that user-provided tags are incomplete and imprecise to some extent. Directly
using them can damage the performance of related applications, such as the
image annotation and retrieval. In this paper, we propose to learn an image
annotation model and refine the user-provided tags simultaneously in a
weakly-supervised manner. The deep neural network is utilized as the image
feature learning and backbone annotation model, while visual consistency,
semantic dependency, and user-error sparsity are introduced as the constraints
at the batch level to alleviate the tag noise. Therefore, our model is highly
flexible and stable to handle large-scale image sets. Experimental results on
two benchmark datasets indicate that our proposed model achieves the best
performance compared to the state-of-the-art methods.

negative:
We propose an architecture for fine-grained visual categorization that
approaches expert human performance in the classification of bird species. Our
architecture first computes an estimate of the object's pose; this is used to
compute local image features which are, in turn, used for classification. The
features are computed by applying deep convolutional nets to image patches that
are located and normalized by the pose. We perform an empirical study of a
number of pose normalization schemes, including an investigation of higher
order geometric warping functions. We propose a novel graph-based clustering
algorithm for learning a compact pose normalization space. We perform a
detailed investigation of state-of-the-art deep convolutional feature
implementations and fine-tuning feature learning for fine-grained
classification. We observe that a model that integrates lower-level feature
layers with pose-normalized extraction routines and higher-level feature layers
with unaligned image features works best. Our experiments advance
state-of-the-art performance on bird species recognition, with a large
improvement of correct classification rates over previous methods (75% vs.
55-65%).

negative:
Previous researchers have considered sentiment analysis as a document
classification task, in which input documents are classified into predefined
sentiment classes. Although there are sentences in a document that support
important evidences for sentiment analysis and sentences that do not, they have
treated the document as a bag of sentences. In other words, they have not
considered the importance of each sentence in the document. To effectively
determine polarity of a document, each sentence in the document should be dealt
with different degrees of importance. To address this problem, we propose a
document-level sentence classification model based on deep neural networks, in
which the importance degrees of sentences in documents are automatically
determined through gate mechanisms. To verify our new sentiment analysis model,
we conducted experiments using the sentiment datasets in the four different
domains such as movie reviews, hotel reviews, restaurant reviews, and music
reviews. In the experiments, the proposed model outperformed previous
state-of-the-art models that do not consider importance differences of
sentences in a document. The experimental results show that the importance of
sentences should be considered in a document-level sentiment classification
task.

negative:
One common trend in image tagging research is to focus on visually relevant
tags, and this tends to ignore the personal and social aspect of tags,
especially on photoblogging websites such as Flickr. Previous work has
correctly identified that many of the tags that users provide on images are not
visually relevant (i.e. representative of the salient content in the image) and
they go on to treat such tags as noise, ignoring that the users chose to
provide those tags over others that could have been more visually relevant.
Another common assumption about user generated tags for images is that the
order of these tags provides no useful information for the prediction of tags
on future images. This assumption also tends to define usefulness in terms of
what is visually relevant to the image. For general tagging or labeling
applications that focus on providing visual information about image content,
these assumptions are reasonable, but when considering personalized image
tagging applications, these assumptions are at best too rigid, ignoring user
choice and preferences.
  We challenge the aforementioned assumptions, and provide a machine learning
approach to the problem of personalized image tagging with the following
contributions: 1.) We reformulate the personalized image tagging problem as a
search/retrieval ranking problem, 2.) We leverage the order of tags, which does
not always reflect visual relevance, provided by the user in the past as a cue
to their tag preferences, similar to click data, 3.) We propose a technique to
augment sparse user tag data (semi-supervision), and 4.) We demonstrate the
efficacy of our method on a subset of Flickr images, showing improvement over
previous state-of-art methods.

negative:
Generative models have made significant progress in the tasks of modeling
complex data distributions such as natural images. The introduction of
Generative Adversarial Networks (GANs) and auto-encoders lead to the
possibility of training on big data sets in an unsupervised manner. However,
for many generative models it is not possible to specify what kind of image
should be generated and it is not possible to translate existing images into
new images of similar domains. Furthermore, models that can perform
image-to-image translation often need distinct models for each domain, making
it hard to scale these systems to multiple domain image-to-image translation.
We introduce a model that can do both, controllable image generation and
image-to-image translation between multiple domains. We split our image
representation into two parts encoding unstructured and structured information
respectively. The latter is designed in a disentangled manner, so that
different parts encode different image characteristics. We train an encoder to
encode images into these representations and use a small amount of labeled data
to specify what kind of information should be encoded in the disentangled part.
A generator is trained to generate images from these representations using the
characteristics provided by the disentangled part of the representation.
Through this we can control what kind of images the generator generates,
translate images between different domains, and even learn unknown
data-generating factors while only using one single model.

negative:
Standard neural machine translation (NMT) is on the assumption of
document-level context independent. Most existing document-level NMT methods
are satisfied with a smattering sense of brief document-level information,
while this work focuses on exploiting detailed document-level context in terms
of multiple forms of document embeddings, which is capable of sufficiently
modeling deeper and richer document-level context. The proposed document-aware
NMT is implemented to enhance the Transformer baseline by introducing both
global and local document-level clues on the source end. Experiments show that
the proposed method significantly improves the translation performance over
strong baselines and other related studies.

negative:
We propose a novel mode of feedback for image search, where a user describes
which properties of exemplar images should be adjusted in order to more closely
match his/her mental model of the image sought. For example, perusing image
results for a query "black shoes", the user might state, "Show me shoe images
like these, but sportier." Offline, our approach first learns a set of ranking
functions, each of which predicts the relative strength of a nameable attribute
in an image (e.g., sportiness). At query time, the system presents the user
with a set of exemplar images, and the user relates them to his/her target
image with comparative statements. Using a series of such constraints in the
multi-dimensional attribute space, our method iteratively updates its relevance
function and re-ranks the database of images. To determine which exemplar
images receive feedback from the user, we present two variants of the approach:
one where the feedback is user-initiated and another where the feedback is
actively system-initiated. In either case, our approach allows a user to
efficiently "whittle away" irrelevant portions of the visual feature space,
using semantic language to precisely communicate her preferences to the system.
We demonstrate our technique for refining image search for people, products,
and scenes, and we show that it outperforms traditional binary relevance
feedback in terms of search speed and accuracy. In addition, the ordinal nature
of relative attributes helps make our active approach efficient -- both
computationally for the machine when selecting the reference images, and for
the user by requiring less user interaction than conventional passive and
active methods.

negative:
In shared autonomy, user input is combined with semi-autonomous control to
achieve a common goal. The goal is often unknown ex-ante, so prior work enables
agents to infer the goal from user input and assist with the task. Such methods
tend to assume some combination of knowledge of the dynamics of the
environment, the user's policy given their goal, and the set of possible goals
the user might target, which limits their application to real-world scenarios.
We propose a deep reinforcement learning framework for model-free shared
autonomy that lifts these assumptions. We use human-in-the-loop reinforcement
learning with neural network function approximation to learn an end-to-end
mapping from environmental observation and user input to agent action values,
with task reward as the only form of supervision. This approach poses the
challenge of following user commands closely enough to provide the user with
real-time action feedback and thereby ensure high-quality user input, but also
deviating from the user's actions when they are suboptimal. We balance these
two needs by discarding actions whose values fall below some threshold, then
selecting the remaining action closest to the user's input. Controlled studies
with users (n = 12) and synthetic pilots playing a video game, and a pilot
study with users (n = 4) flying a real quadrotor, demonstrate the ability of
our algorithm to assist users with real-time control tasks in which the agent
cannot directly access the user's private information through observations, but
receives a reward signal and user input that both depend on the user's intent.
The agent learns to assist the user without access to this private information,
implicitly inferring it from the user's input. This paper is a proof of concept
that illustrates the potential for deep reinforcement learning to enable
flexible and practical assistive systems.

negative:
Prior work on training generative Visual Dialog models with reinforcement
learning(Das et al.) has explored a Qbot-Abot image-guessing game and shown
that this 'self-talk' approach can lead to improved performance at the
downstream dialog-conditioned image-guessing task. However, this improvement
saturates and starts degrading after a few rounds of interaction, and does not
lead to a better Visual Dialog model. We find that this is due in part to
repeated interactions between Qbot and Abot during self-talk, which are not
informative with respect to the image. To improve this, we devise a simple
auxiliary objective that incentivizes Qbot to ask diverse questions, thus
reducing repetitions and in turn enabling Abot to explore a larger state space
during RL ie. be exposed to more visual concepts to talk about, and varied
questions to answer. We evaluate our approach via a host of automatic metrics
and human studies, and demonstrate that it leads to better dialog, ie. dialog
that is more diverse (ie. less repetitive), consistent (ie. has fewer
conflicting exchanges), fluent (ie. more human-like),and detailed, while still
being comparably image-relevant as prior work and ablations.

negative:
To achieve the long-term goal of machines being able to engage humans in
conversation, our models should captivate the interest of their speaking
partners. Communication grounded in images, whereby a dialogue is conducted
based on a given photo, is a setup naturally appealing to humans (Hu et al.,
2014). In this work we study large-scale architectures and datasets for this
goal. We test a set of neural architectures using state-of-the-art image and
text representations, considering various ways to fuse the components. To test
such models, we collect a dataset of grounded human-human conversations, where
speakers are asked to play roles given a provided emotional mood or style, as
the use of such traits is also a key factor in engagingness (Guo et al., 2019).
Our dataset, Image-Chat, consists of 202k dialogues over 202k images using 215
possible style traits. Automatic metrics and human evaluations of engagingness
show the efficacy of our approach; in particular, we obtain state-of-the-art
performance on the existing IGC task, and our best performing model is almost
on par with humans on the Image-Chat test set (preferred 47.7% of the time).

negative:
In this paper we consider the problem of continuously discovering image
contents by actively asking image based questions and subsequently answering
the questions being asked. The key components include a Visual Question
Generation (VQG) module and a Visual Question Answering module, in which
Recurrent Neural Networks (RNN) and Convolutional Neural Network (CNN) are
used. Given a dataset that contains images, questions and their answers, both
modules are trained at the same time, with the difference being VQG uses the
images as input and the corresponding questions as output, while VQA uses
images and questions as input and the corresponding answers as output. We
evaluate the self talk process subjectively using Amazon Mechanical Turk, which
show effectiveness of the proposed method.

negative:
State-of-the-art systems in deep question answering proceed as follows: (1)
an initial document retrieval selects relevant documents, which (2) are then
processed by a neural network in order to extract the final answer. Yet the
exact interplay between both components is poorly understood, especially
concerning the number of candidate documents that should be retrieved. We show
that choosing a static number of documents -- as used in prior research --
suffers from a noise-information trade-off and yields suboptimal results. As a
remedy, we propose an adaptive document retrieval model. This learns the
optimal candidate number for document retrieval, conditional on the size of the
corpus and the query. We report extensive experimental results showing that our
adaptive approach outperforms state-of-the-art methods on multiple benchmark
datasets, as well as in the context of corpora with variable sizes.

negative:
In this paper, we introduce Iterative Text Summarization (ITS), an
iteration-based model for supervised extractive text summarization, inspired by
the observation that it is often necessary for a human to read an article
multiple times in order to fully understand and summarize its contents. Current
summarization approaches read through a document only once to generate a
document representation, resulting in a sub-optimal representation. To address
this issue we introduce a model which iteratively polishes the document
representation on many passes through the document. As part of our model, we
also introduce a selective reading mechanism that decides more accurately the
extent to which each sentence in the model should be updated. Experimental
results on the CNN/DailyMail and DUC2002 datasets demonstrate that our model
significantly outperforms state-of-the-art extractive systems when evaluated by
machines and by humans.

negative:
Today all kind of information is getting digitized and along with all this
digitization, the huge archive of various kinds of documents is being digitized
too. We know that, Optical Character Recognition is the method through which,
newspapers and other paper documents convert into digital resources. But, it is
a fact that this method works on texts only. As a result, if we try to process
any document which contains non-textual zones, then we will get garbage texts
as output. That is why; in order to digitize documents properly they should be
prepossessed carefully. And while preprocessing, segmenting document in
different regions according to the category properly is most important. But,
the Optical Character Recognition processes available for Bangla language have
no such algorithm that can categorize a newspaper/book page fully. So we worked
to decompose a document into its several parts like headlines, sub headlines,
columns, images etc. And if the input is skewed and rotated, then the input was
also deskewed and de-rotated. To decompose any Bangla document we found out the
edges of the input image. Then we find out the horizontal and vertical area of
every pixel where it lies in. Later on the input image was cut according to
these areas. Then we pick each and every sub image and found out their
height-width ratio, line height. Then according to these values the sub images
were categorized. To deskew the image we found out the skew angle and de skewed
the image according to this angle. To de-rotate the image we used the line
height, matra line, pixel ratio of matra line.

negative:
We assume that we are given a time series of data from a dynamical system and
our task is to learn the flow map of the dynamical system. We present a
collection of results on how to enforce constraints coming from the dynamical
system in order to accelerate the training of deep neural networks to represent
the flow map of the system as well as increase their predictive ability. In
particular, we provide ways to enforce constraints during training for all
three major modes of learning, namely supervised, unsupervised and
reinforcement learning. In general, the dynamic constraints need to include
terms which are analogous to memory terms in model reduction formalisms. Such
memory terms act as a restoring force which corrects the errors committed by
the learned flow map during prediction.
  For supervised learning, the constraints are added to the objective function.
For the case of unsupervised learning, in particular generative adversarial
networks, the constraints are introduced by augmenting the input of the
discriminator. Finally, for the case of reinforcement learning and in
particular actor-critic methods, the constraints are added to the reward
function. In addition, for the reinforcement learning case, we present a novel
approach based on homotopy of the action-value function in order to stabilize
and accelerate training. We use numerical results for the Lorenz system to
illustrate the various constructions.

negative:
With the advent of mobile and hand-held cameras, document images have found
their way into almost every domain. Dewarping of these images for the removal
of perspective distortions and folds is essential so that they can be
understood by document recognition algorithms. For this, we propose an
end-to-end CNN architecture that can produce distortion free document images
from warped documents it takes as input. We train this model on warped document
images simulated synthetically to compensate for lack of enough natural data.
Our method is novel in the use of a bifurcated decoder with shared weights to
prevent intermingling of grid coordinates, in the use of residual networks in
the U-Net skip connections to allow flow of data from different receptive
fields in the model, and in the use of a gated network to help the model focus
on structure and line level detail of the document image. We evaluate our
method on the DocUNet dataset, a benchmark in this domain, and obtain results
comparable to state-of-the-art methods.

negative:
We present a contrasting learning approach with data augmentation techniques
to learn document representations in an unsupervised manner. Inspired by recent
contrastive self-supervised learning algorithms used for image and NLP
pretraining, we hypothesize that high-quality document embedding should be
invariant to diverse paraphrases that preserve the semantics of the original
document. With different backbones and contrastive learning frameworks, our
study reveals the enormous benefits of contrastive augmentation for document
representation learning with two additional insights: 1) including data
augmentation in a contrastive way can substantially improve the embedding
quality in unsupervised document representation learning, and 2) in general,
stochastic augmentations generated by simple word-level manipulation work much
better than sentence-level and document-level ones. We plug our method into a
classifier and compare it with a broad range of baseline methods on six
benchmark datasets. Our method can decrease the classification error rate by up
to 6.4% over the SOTA approaches on the document classification task, matching
or even surpassing fully-supervised methods.

negative:
Computation of document image quality metrics often depends upon the
availability of a ground truth image corresponding to the document. This limits
the applicability of quality metrics in applications such as hyperparameter
optimization of image processing algorithms that operate on-the-fly on unseen
documents. This work proposes the use of surrogate models to learn the behavior
of a given document quality metric on existing datasets where ground truth
images are available. The trained surrogate model can later be used to predict
the metric value on previously unseen document images without requiring access
to ground truth images. The surrogate model is empirically evaluated on the
Document Image Binarization Competition (DIBCO) and the Handwritten Document
Image Binarization Competition (H-DIBCO) datasets.

negative:
Recent progress in pretrained Transformer-based language models has shown
great success in learning contextual representation of text. However, due to
the quadratic self-attention complexity, most of the pretrained Transformers
models can only handle relatively short text. It is still a challenge when it
comes to modeling very long documents. In this work, we propose to use a graph
attention network on top of the available pretrained Transformers model to
learn document embeddings. This graph attention network allows us to leverage
the high-level semantic structure of the document. In addition, based on our
graph document model, we design a simple contrastive learning strategy to
pretrain our models on a large amount of unlabeled corpus. Empirically, we
demonstrate the effectiveness of our approaches in document classification and
document retrieval tasks.

negative:
We propose a method for automatically answering questions about images by
bringing together recent advances from natural language processing and computer
vision. We combine discrete reasoning with uncertain predictions by a
multi-world approach that represents uncertainty about the perceived world in a
bayesian framework. Our approach can handle human questions of high complexity
about realistic scenes and replies with range of answer like counts, object
classes, instances and lists of them. The system is directly trained from
question-answer pairs. We establish a first benchmark for this task that can be
seen as a modern attempt at a visual turing test.

negative:
Most chatbot literature that focuses on improving the fluency and coherence
of a chatbot, is dedicated to making chatbots more human-like. However, very
little work delves into what really separates humans from chatbots -- humans
intrinsically understand the effect their responses have on the interlocutor
and often respond with an intention such as proposing an optimistic view to
make the interlocutor feel better. This paper proposes an innovative framework
to train chatbots to possess human-like intentions. Our framework includes a
guiding chatbot and an interlocutor model that plays the role of humans. The
guiding chatbot is assigned an intention and learns to induce the interlocutor
to reply with responses matching the intention, for example, long responses,
joyful responses, responses with specific words, etc. We examined our framework
using three experimental setups and evaluated the guiding chatbot with four
different metrics to demonstrate flexibility and performance advantages.
Additionally, we performed trials with human interlocutors to substantiate the
guiding chatbot's effectiveness in influencing the responses of humans to a
certain extent. Code will be made available to the public.

negative:
In industrial data analytics, one of the fundamental problems is to utilize
the temporal correlation of the industrial data to make timely predictions in
the production process, such as fault prediction and yield prediction. However,
the traditional prediction models are fixed while the conditions of the
machines change over time, thus making the errors of predictions increase with
the lapse of time. In this paper, we propose a general data renewal model to
deal with it. Combined with the similarity function and the loss function, it
estimates the time of updating the existing prediction model, then updates it
according to the evaluation function iteratively and adaptively. We have
applied the data renewal model to two prediction algorithms. The experiments
demonstrate that the data renewal model can effectively identify the changes of
data, update and optimize the prediction model so as to improve the accuracy of
prediction.

negative:
Line separators are used to segregate text-lines from one another in document
image analysis. Finding the separator points at every line terminal in a
document image would enable text-line segmentation. In particular, identifying
the separators in handwritten text could be a thrilling exercise. Obviously it
would be challenging to perform this in the compressed version of a document
image and that is the proposed objective in this research. Such an effort would
prevent the computational burden of decompressing a document for text-line
segmentation. Since document images are generally compressed using run length
encoding (RLE) technique as per the CCITT standards, the first column in the
RLE will be a white column. The value (depth) in the white column is very low
when a particular line is a text line and the depth could be larger at the
point of text line separation. A longer consecutive sequence of such larger
depth should indicate the gap between the text lines, which provides the
separator region. In case of over separation and under separation issues,
corrective actions such as deletion and insertion are suggested respectively.
An extensive experimentation is conducted on the compressed images of the
benchmark datasets of ICDAR13 and Alireza et al [17] to demonstrate the
efficacy.

negative:
Keyword extraction is an important document process that aims at finding a
small set of terms that concisely describe a document's topics. The most
popular state-of-the-art unsupervised approaches belong to the family of the
graph-based methods that build a graph-of-words and use various centrality
measures to score the nodes (candidate keywords). In this work, we follow a
different path to detect the keywords from a text document by modeling the main
distribution of the document's words using local word vector representations.
Then, we rank the candidates based on their position in the text and the
distance between the corresponding local vectors and the main distribution's
center. We confirm the high performance of our approach compared to strong
baselines and state-of-the-art unsupervised keyword extraction methods, through
an extended experimental study, investigating the properties of the local
representations.

negative:
We propose a new probabilistic graphical model that jointly models the
difficulties of questions, the abilities of participants and the correct
answers to questions in aptitude testing and crowdsourcing settings. We devise
an active learning/adaptive testing scheme based on a greedy minimization of
expected model entropy, which allows a more efficient resource allocation by
dynamically choosing the next question to be asked based on the previous
responses. We present experimental results that confirm the ability of our
model to infer the required parameters and demonstrate that the adaptive
testing scheme requires fewer questions to obtain the same accuracy as a static
test scenario.

negative:
(abridged) We describe an image-comparison technique of Heidemann and Ritter
that uses image compression, and is capable of: (i) detecting novel textures in
a series of images, as well as of: (ii) alerting the user to the similarity of
a new image to a previously-observed texture. This image-comparison technique
has been implemented and tested using our Astrobiology Phone-cam system, which
employs Bluetooth communication to send images to a local laptop server in the
field for the image-compression analysis. We tested the system in a field site
displaying a heterogeneous suite of sandstones, limestones, mudstones and
coalbeds. Some of the rocks are partly covered with lichen. The image-matching
procedure of this system performed very well with data obtained through our
field test, grouping all images of yellow lichens together and grouping all
images of a coal bed together, and giving a 91% accuracy for similarity
detection. Such similarity detection could be employed to make maps of
different geological units. The novelty-detection performance of our system was
also rather good (a 64% accuracy). Such novelty detection may become valuable
in searching for new geological units, which could be of astrobiological
interest. The image-comparison technique is an unsupervised technique that is
not capable of directly classifying an image as containing a particular
geological feature; labeling of such geological features is done post facto by
human geologists associated with this study, for the purpose of analyzing the
system's performance. By providing more advanced capabilities for similarity
detection and novelty detection, this image-compression technique could be
useful in giving more scientific autonomy to robotic planetary rovers, and in
assisting human astronauts in their geological exploration and assessment.

negative:
Smart reply systems have been developed for various messaging platforms. In
this paper, we introduce Uber's smart reply system: one-click-chat (OCC), which
is a key enhanced feature on top of the Uber in-app chat system. It enables
driver-partners to quickly respond to rider messages using smart replies. The
smart replies are dynamically selected according to conversation content using
machine learning algorithms. Our system consists of two major components:
intent detection and reply retrieval, which are very different from standard
smart reply systems where the task is to directly predict a reply. It is
designed specifically for mobile applications with short and non-canonical
messages. Reply retrieval utilizes pairings between intent and reply based on
their popularity in chat messages as derived from historical data. For intent
detection, a set of embedding and classification techniques are experimented
with, and we choose to deploy a solution using unsupervised distributed
embedding and nearest-neighbor classifier. It has the advantage of only
requiring a small amount of labeled training data, simplicity in developing and
deploying to production, and fast inference during serving and hence highly
scalable. At the same time, it performs comparably with deep learning
architectures such as word-level convolutional neural network. Overall, the
system achieves a high accuracy of 76% on intent detection. Currently, the
system is deployed in production for English-speaking countries and 71% of
in-app communications between riders and driver-partners adopted the smart
replies to speedup the communication process.

negative:
Image Processing, Optimization and Prediction of an Image play a key role in
Computer Science. Image processing provides a way to analyze and identify an
image .Many areas like medical image processing, Satellite images, natural
images and artificial images requires lots of analysis and research on
optimization. In Image Optimization and Prediction we are combining the
features of Query Optimization, Image Processing and Prediction . Image
optimization is used in Pattern analysis, object recognition, in medical Image
processing to predict the type of diseases, in satellite images for predicting
weather forecast, availability of water or mineral etc. Image Processing,
Optimization and analysis is a wide open area for research .Lots of research
has been conducted in the area of Image analysis and many techniques are
available for image analysis but, a single technique is not yet identified for
image analysis and prediction .our research is focused on identifying a global
technique for image analysis and Prediction.

negative:
We investigate the difficulty levels of questions in reading comprehension
datasets such as SQuAD, and propose a new question generation setting, named
Difficulty-controllable Question Generation (DQG). Taking as input a sentence
in the reading comprehension paragraph and some of its text fragments (i.e.,
answers) that we want to ask questions about, a DQG method needs to generate
questions each of which has a given text fragment as its answer, and meanwhile
the generation is under the control of specified difficulty labels---the output
questions should satisfy the specified difficulty as much as possible. To solve
this task, we propose an end-to-end framework to generate questions of
designated difficulty levels by exploring a few important intuitions. For
evaluation, we prepared the first dataset of reading comprehension questions
with difficulty labels. The results show that the question generated by our
framework not only have better quality under the metrics like BLEU, but also
comply with the specified difficulty labels.

negative:
The ability to describe images with natural language sentences is the
hallmark for image and language understanding. Such a system has wide ranging
applications such as annotating images and using natural sentences to search
for images.In this project we focus on the task of bidirectional image
retrieval: such asystem is capable of retrieving an image based on a sentence
(image search) andretrieve sentence based on an image query (image annotation).
We present asystem based on a global ranking objective function which uses a
combinationof convolutional neural networks (CNN) and multi layer perceptrons
(MLP).It takes a pair of image and sentence and processes them in different
channels,finally embedding it into a common multimodal vector space. These
embeddingsencode abstract semantic information about the two inputs and can be
comparedusing traditional information retrieval approaches. For each such pair,
the modelreturns a score which is interpretted as a similarity metric. If this
score is high,the image and sentence are likely to convey similar meaning, and
if the score is low then they are likely not to.
  The visual input is modeled via deep convolutional neural network. On
theother hand we explore three models for the textual module. The first one
isbag of words with an MLP. The second one uses n-grams (bigram, trigrams,and a
combination of trigram & skip-grams) with an MLP. The third is morespecialized
deep network specific for modeling variable length sequences (SSE).We report
comparable performance to recent work in the field, even though ouroverall
model is simpler. We also show that the training time choice of how wecan
generate our negative samples has a significant impact on performance, and can
be used to specialize the bi-directional system in one particular task.

negative:
Standard neural machine translation (NMT) is on the assumption that the
document-level context is independent. Most existing document-level NMT
approaches are satisfied with a smattering sense of global document-level
information, while this work focuses on exploiting detailed document-level
context in terms of a memory network. The capacity of the memory network that
detecting the most relevant part of the current sentence from memory renders a
natural solution to model the rich document-level context. In this work, the
proposed document-aware memory network is implemented to enhance the
Transformer NMT baseline. Experiments on several tasks show that the proposed
method significantly improves the NMT performance over strong Transformer
baselines and other related studies.

negative:
In this paper, we show that certain phrases although not present in a given
question/query, play a very important role in answering the question. Exploring
the role of such phrases in answering questions not only reduces the dependency
on matching question phrases for extracting answers, but also improves the
quality of the extracted answers. Here matching question phrases means phrases
which co-occur in given question and candidate answers. To achieve the above
discussed goal, we introduce a bigram-based word graph model populated with
semantic and topical relatedness of terms in the given document. Next, we apply
an improved version of ranking with a prior-based approach, which ranks all
words in the candidate document with respect to a set of root words (i.e.
non-stopwords present in the question and in the candidate document). As a
result, terms logically related to the root words are scored higher than terms
that are not related to the root words. Experimental results show that our
devised system performs better than state-of-the-art for the task of answering
Why-questions.

negative:
As the wide adoption of intelligent chatbot in human daily life, user demands
for such systems evolve from basic task-solving conversations to more casual
and friend-like communication. To meet the user needs and build emotional bond
with users, it is essential for social chatbots to incorporate more human-like
and advanced linguistic features. In this paper, we investigate the usage of a
commonly used rhetorical device by human -- metaphor for social chatbot. Our
work first designs a metaphor generation framework, which generates topic-aware
and novel figurative sentences. By embedding the framework into a chatbot
system, we then enables the chatbot to communicate with users using figurative
language. Human annotators validate the novelty and properness of the generated
metaphors. More importantly, we evaluate the effects of employing metaphors in
human-chatbot conversations. Experiments indicate that our system effectively
arouses user interests in communicating with our chatbot, resulting in
significantly longer human-chatbot conversations.

negative:
With the increase of information, document classification as one of the
methods of text mining, plays vital role in many management and organizing
information. Document classification is the process of assigning a document to
one or more predefined category labels. Document classification includes
different parts such as text processing, term selection, term weighting and
final classification. The accuracy of document classification is very
important. Thus improvement in each part of classification should lead to
better results and higher precision. Term weighting has a great impact on the
accuracy of the classification. Most of the existing weighting methods exploit
the statistical information of terms in documents and do not consider semantic
relations between words. In this paper, an automated document classification
system is presented that uses a novel term weighting method based on semantic
relations between terms. To evaluate the proposed method, three standard
Persian corpuses are used. Experiment results show 2 to 4 percent improvement
in classification accuracy compared with the best previous designed system for
Persian documents.

negative:
Generating realistic images is difficult, and many formulations for this task
have been proposed recently. If we restrict the task to that of generating a
particular class of images, however, the task becomes more tractable. That is
to say, instead of generating an arbitrary image as a sample from the manifold
of natural images, we propose to sample images from a particular "subspace" of
natural images, directed by a low-resolution image from the same subspace. The
problem we address, while close to the formulation of the single-image
super-resolution problem, is in fact rather different. Single image
super-resolution is the task of predicting the image closest to the ground
truth from a relatively low resolution image. We propose to produce samples of
high resolution images given extremely small inputs with a new method called
Latent Adversarial Generator (LAG). In our generative sampling framework, we
only use the input (possibly of very low-resolution) to direct what class of
samples the network should produce. As such, the output of our algorithm is not
a unique image that relates to the input, but rather a possible se} of related
images sampled from the manifold of natural images. Our method learns
exclusively in the latent space of the adversary using perceptual loss -- it
does not have a pixel loss.

negative:
Text Mining is a field that aims at extracting information from textual data.
One of the challenges of such field of study comes from the pre-processing
stage in which a vector (and structured) representation should be extracted
from unstructured data. The common extraction creates large and sparse vectors
representing the importance of each term to a document. As such, this usually
leads to the curse-of-dimensionality that plagues most machine learning
algorithms. To cope with this issue, in this paper we propose a new supervised
feature extraction and reduction algorithm, named DCDistance, that creates
features based on the distance between a document to a representative of each
class label. As such, the proposed technique can reduce the features set in
more than 99% of the original set. Additionally, this algorithm was also
capable of improving the classification accuracy over a set of benchmark
datasets when compared to traditional and state-of-the-art features selection
algorithms.

negative:
There is more to images than their objective physical content: for example,
advertisements are created to persuade a viewer to take a certain action. We
propose the novel problem of automatic advertisement understanding. To enable
research on this problem, we create two datasets: an image dataset of 64,832
image ads, and a video dataset of 3,477 ads. Our data contains rich annotations
encompassing the topic and sentiment of the ads, questions and answers
describing what actions the viewer is prompted to take and the reasoning that
the ad presents to persuade the viewer ("What should I do according to this ad,
and why should I do it?"), and symbolic references ads make (e.g. a dove
symbolizes peace). We also analyze the most common persuasive strategies ads
use, and the capabilities that computer vision systems should have to
understand these strategies. We present baseline classification results for
several prediction tasks, including automatically answering questions about the
messages of the ads.

negative:
We have seen great progress in basic perceptual tasks such as object
recognition and detection. However, AI models still fail to match humans in
high-level vision tasks due to the lack of capacities for deeper reasoning.
Recently the new task of visual question answering (QA) has been proposed to
evaluate a model's capacity for deep image understanding. Previous works have
established a loose, global association between QA sentences and images.
However, many questions and answers, in practice, relate to local regions in
the images. We establish a semantic link between textual descriptions and image
regions by object-level grounding. It enables a new type of QA with visual
answers, in addition to textual answers used in previous work. We study the
visual QA tasks in a grounded setting with a large collection of 7W
multiple-choice QA pairs. Furthermore, we evaluate human performance and
several baseline models on the QA tasks. Finally, we propose a novel LSTM model
with spatial attention to tackle the 7W QA tasks.

negative:
Generating natural language descriptions of images is an important capability
for a robot or other visual-intelligence driven AI agent that may need to
communicate with human users about what it is seeing. Such image captioning
methods are typically trained by maximising the likelihood of ground-truth
annotated caption given the image. While simple and easy to implement, this
approach does not directly maximise the language quality metrics we care about
such as CIDEr. In this paper we investigate training image captioning methods
based on actor-critic reinforcement learning in order to directly optimise
non-differentiable quality metrics of interest. By formulating a per-token
advantage and value computation strategy in this novel reinforcement learning
based captioning model, we show that it is possible to achieve the state of the
art performance on the widely used MSCOCO benchmark.

negative:
Deep reading models for question-answering have demonstrated promising
performance over the last couple of years. However current systems tend to
learn how to cleverly extract a span of the source document, based on its
similarity with the question, instead of seeking for the appropriate answer.
Indeed, a reading machine should be able to detect relevant passages in a
document regarding a question, but more importantly, it should be able to
reason over the important pieces of the document in order to produce an answer
when it is required. To motivate this purpose, we present ReviewQA, a
question-answering dataset based on hotel reviews. The questions of this
dataset are linked to a set of relational understanding competencies that we
expect a model to master. Indeed, each question comes with an associated type
that characterizes the required competency. With this framework, it is possible
to benchmark the main families of models and to get an overview of what are the
strengths and the weaknesses of a given model on the set of tasks evaluated in
this dataset. Our corpus contains more than 500.000 questions in natural
language over 100.000 hotel reviews. Our setup is projective, the answer of a
question does not need to be extracted from a document, like in most of the
recent datasets, but selected among a set of candidates that contains all the
possible answers to the questions of the dataset. Finally, we present several
baselines over this dataset.

negative:
Document categorization, which aims to assign a topic label to each document,
plays a fundamental role in a wide variety of applications. Despite the success
of existing studies in conventional supervised document classification, they
are less concerned with two real problems: (1) the presence of metadata: in
many domains, text is accompanied by various additional information such as
authors and tags. Such metadata serve as compelling topic indicators and should
be leveraged into the categorization framework; (2) label scarcity: labeled
training samples are expensive to obtain in some cases, where categorization
needs to be performed using only a small set of annotated data. In recognition
of these two challenges, we propose MetaCat, a minimally supervised framework
to categorize text with metadata. Specifically, we develop a generative process
describing the relationships between words, documents, labels, and metadata.
Guided by the generative model, we embed text and metadata into the same
semantic space to encode heterogeneous signals. Then, based on the same
generative process, we synthesize training samples to address the bottleneck of
label scarcity. We conduct a thorough evaluation on a wide range of datasets.
Experimental results prove the effectiveness of MetaCat over many competitive
baselines.

negative:
Neural machine translation (NMT) has arguably achieved human level parity
when trained and evaluated at the sentence-level. Document-level neural machine
translation has received less attention and lags behind its sentence-level
counterpart. The majority of the proposed document-level approaches investigate
ways of conditioning the model on several source or target sentences to capture
document context. These approaches require training a specialized NMT model
from scratch on parallel document-level corpora. We propose an approach that
doesn't require training a specialized model on parallel document-level corpora
and is applied to a trained sentence-level NMT model at decoding time. We
process the document from left to right multiple times and self-train the
sentence-level model on pairs of source sentences and generated translations.
Our approach reinforces the choices made by the model, thus making it more
likely that the same choices will be made in other sentences in the document.
We evaluate our approach on three document-level datasets: NIST
Chinese-English, WMT'19 Chinese-English and OpenSubtitles English-Russian. We
demonstrate that our approach has higher BLEU score and higher human preference
than the baseline. Qualitative analysis of our approach shows that choices made
by model are consistent across the document.

negative:
In this paper, we study the problem of answering visual analogy questions.
These questions take the form of image A is to image B as image C is to what.
Answering these questions entails discovering the mapping from image A to image
B and then extending the mapping to image C and searching for the image D such
that the relation from A to B holds for C to D. We pose this problem as
learning an embedding that encourages pairs of analogous images with similar
transformations to be close together using convolutional neural networks with a
quadruple Siamese architecture. We introduce a dataset of visual analogy
questions in natural images, and show first results of its kind on solving
analogy questions on natural images.

negative:
We explore using multilingual document embeddings for nearest neighbor mining
of parallel data. Three document-level representations are investigated: (i)
document embeddings generated by simply averaging multilingual sentence
embeddings; (ii) a neural bag-of-words (BoW) document encoding model; (iii) a
hierarchical multilingual document encoder (HiDE) that builds on our
sentence-level model. The results show document embeddings derived from
sentence-level averaging are surprisingly effective for clean datasets, but
suggest models trained hierarchically at the document-level are more effective
on noisy data. Analysis experiments demonstrate our hierarchical models are
very robust to variations in the underlying sentence embedding quality. Using
document embeddings trained with HiDE achieves state-of-the-art performance on
United Nations (UN) parallel document mining, 94.9% P@1 for en-fr and 97.3% P@1
for en-es.

negative:
COVID-19 has resulted in an ongoing pandemic and as of 12 June 2020, has
caused more than 7.4 million cases and over 418,000 deaths. The highly dynamic
and rapidly evolving situation with COVID-19 has made it difficult to access
accurate, on-demand information regarding the disease. Online communities,
forums, and social media provide potential venues to search for relevant
questions and answers, or post questions and seek answers from other members.
However, due to the nature of such sites, there are always a limited number of
relevant questions and responses to search from, and posted questions are
rarely answered immediately. With the advancements in the field of natural
language processing, particularly in the domain of language models, it has
become possible to design chatbots that can automatically answer consumer
questions. However, such models are rarely applied and evaluated in the
healthcare domain, to meet the information needs with accurate and up-to-date
healthcare data. In this paper, we propose to apply a language model for
automatically answering questions related to COVID-19 and qualitatively
evaluate the generated responses. We utilized the GPT-2 language model and
applied transfer learning to retrain it on the COVID-19 Open Research Dataset
(CORD-19) corpus. In order to improve the quality of the generated responses,
we applied 4 different approaches, namely tf-idf, BERT, BioBERT, and USE to
filter and retain relevant sentences in the responses. In the performance
evaluation step, we asked two medical experts to rate the responses. We found
that BERT and BioBERT, on average, outperform both tf-idf and USE in
relevance-based sentence filtering tasks. Additionally, based on the chatbot,
we created a user-friendly interactive web application to be hosted online.

negative:
Explaining the predictions of AI models is paramount in safety-critical
applications, such as in legal or medical domains. One form of explanation for
a prediction is an extractive rationale, i.e., a subset of features of an
instance that lead the model to give its prediction on the instance. Previous
works on generating extractive rationales usually employ a two-phase model: a
selector that selects the most important features (i.e., the rationale)
followed by a predictor that makes the prediction based exclusively on the
selected features. One disadvantage of these works is that the main signal for
learning to select features comes from the comparison of the answers given by
the predictor and the ground-truth answers. In this work, we propose to squeeze
more information from the predictor via an information calibration method. More
precisely, we train two models jointly: one is a typical neural model that
solves the task at hand in an accurate but black-box manner, and the other is a
selector-predictor model that additionally produces a rationale for its
prediction. The first model is used as a guide to the second model. We use an
adversarial-based technique to calibrate the information extracted by the two
models such that the difference between them is an indicator of the missed or
over-selected features. In addition, for natural language tasks, we propose to
use a language-model-based regularizer to encourage the extraction of fluent
rationales. Experimental results on a sentiment analysis task as well as on
three tasks from the legal domain show the effectiveness of our approach to
rationale extraction.

negative:
This paper summarizes our entries to both subtasks of the first DialDoc
shared task which focuses on the agent response prediction task in
goal-oriented document-grounded dialogs. The task is split into two subtasks:
predicting a span in a document that grounds an agent turn and generating an
agent response based on a dialog and grounding document. In the first subtask,
we restrict the set of valid spans to the ones defined in the dataset, use a
biaffine classifier to model spans, and finally use an ensemble of different
models. For the second subtask, we use a cascaded model which grounds the
response prediction on the predicted span instead of the full document. With
these approaches, we obtain significant improvements in both subtasks compared
to the baseline.

negative:
Automatically describing the content of an image is a fundamental problem in
artificial intelligence that connects computer vision and natural language
processing. In this paper, we present a generative model based on a deep
recurrent architecture that combines recent advances in computer vision and
machine translation and that can be used to generate natural sentences
describing an image. The model is trained to maximize the likelihood of the
target description sentence given the training image. Experiments on several
datasets show the accuracy of the model and the fluency of the language it
learns solely from image descriptions. Our model is often quite accurate, which
we verify both qualitatively and quantitatively. Finally, given the recent
surge of interest in this task, a competition was organized in 2015 using the
newly released COCO dataset. We describe and analyze the various improvements
we applied to our own baseline and show the resulting performance in the
competition, which we won ex-aequo with a team from Microsoft Research, and
provide an open source implementation in TensorFlow.

negative:
We introduce the first goal-driven training for visual question answering and
dialog agents. Specifically, we pose a cooperative 'image guessing' game
between two agents -- Qbot and Abot -- who communicate in natural language
dialog so that Qbot can select an unseen image from a lineup of images. We use
deep reinforcement learning (RL) to learn the policies of these agents
end-to-end -- from pixels to multi-agent multi-round dialog to game reward.
  We demonstrate two experimental results.
  First, as a 'sanity check' demonstration of pure RL (from scratch), we show
results on a synthetic world, where the agents communicate in ungrounded
vocabulary, i.e., symbols with no pre-specified meanings (X, Y, Z). We find
that two bots invent their own communication protocol and start using certain
symbols to ask/answer about certain visual attributes (shape/color/style).
Thus, we demonstrate the emergence of grounded language and communication among
'visual' dialog agents with no human supervision.
  Second, we conduct large-scale real-image experiments on the VisDial dataset,
where we pretrain with supervised dialog data and show that the RL 'fine-tuned'
agents significantly outperform SL agents. Interestingly, the RL Qbot learns to
ask questions that Abot is good at, ultimately resulting in more informative
dialog and a better team.

negative:
Neural Networks and Deep Learning have seen an upsurge of research in the
past decade due to the improved results. Generates text from the given image is
a crucial task that requires the combination of both sectors which are computer
vision and natural language processing in order to understand an image and
represent it using a natural language. However existing works have all been
done on a particular lingual domain and on the same set of data. This leads to
the systems being developed to perform poorly on images that belong to specific
locales' geographical context. TextMage is a system that is capable of
understanding visual scenes that belong to the Bangladeshi geographical context
and use its knowledge to represent what it understands in Bengali. Hence, we
have trained a model on our previously developed and published dataset named
BanglaLekhaImageCaptions. This dataset contains 9,154 images along with two
annotations for each image. In order to access performance, the proposed model
has been implemented and evaluated.

negative:
It is known that $O(N)$ parameters are sufficient for neural networks to
memorize arbitrary $N$ input-label pairs. By exploiting depth, we show that
$O(N^{2/3})$ parameters suffice to memorize $N$ pairs, under a mild condition
on the separation of input points. In particular, deeper networks (even with
width $3$) are shown to memorize more pairs than shallow networks, which also
agrees with the recent line of works on the benefits of depth for function
approximation. We also provide empirical results that support our theoretical
findings.

negative:
We consider the task of learning to estimate human pose in still images. In
order to avoid the high cost of full supervision, we propose to use a diverse
data set, which consists of two types of annotations: (i) a small number of
images are labeled using the expensive ground-truth pose; and (ii) other images
are labeled using the inexpensive action label. As action information helps
narrow down the pose of a human, we argue that this approach can help reduce
the cost of training without significantly affecting the accuracy. To
demonstrate this we design a probabilistic framework that employs two
distributions: (i) a conditional distribution to model the uncertainty over the
human pose given the image and the action; and (ii) a prediction distribution,
which provides the pose of an image without using any action information. We
jointly estimate the parameters of the two aforementioned distributions by
minimizing their dissimilarity coefficient, as measured by a task-specific loss
function. During both training and testing, we only require an efficient
sampling strategy for both the aforementioned distributions. This allows us to
use deep probabilistic networks that are capable of providing accurate pose
estimates for previously unseen images. Using the MPII data set, we show that
our approach outperforms baseline methods that either do not use the diverse
annotations or rely on pointwise estimates of the pose.

negative:
Document date is essential for many important tasks, such as document
retrieval, summarization, event detection, etc. While existing approaches for
these tasks assume accurate knowledge of the document date, this is not always
available, especially for arbitrary documents from the Web. Document Dating is
a challenging problem which requires inference over the temporal structure of
the document. Prior document dating systems have largely relied on handcrafted
features while ignoring such document internal structures. In this paper, we
propose NeuralDater, a Graph Convolutional Network (GCN) based document dating
approach which jointly exploits syntactic and temporal graph structures of
document in a principled way. To the best of our knowledge, this is the first
application of deep learning for the problem of document dating. Through
extensive experiments on real-world datasets, we find that NeuralDater
significantly outperforms state-of-the-art baseline by 19% absolute (45%
relative) accuracy points.

negative:
The increasing amount of online content motivated the development of
multi-document summarization methods. In this work, we explore straightforward
approaches to extend single-document summarization methods to multi-document
summarization. The proposed methods are based on the hierarchical combination
of single-document summaries, and achieves state of the art results.

negative:
We propose a novel approach to identify the difficulty of visual questions
for Visual Question Answering (VQA) without direct supervision or annotations
to the difficulty. Prior works have considered the diversity of ground-truth
answers of human annotators. In contrast, we analyze the difficulty of visual
questions based on the behavior of multiple different VQA models. We propose to
cluster the entropy values of the predicted answer distributions obtained by
three different models: a baseline method that takes as input images and
questions, and two variants that take as input images only and questions only.
We use a simple k-means to cluster the visual questions of the VQA v2
validation set. Then we use state-of-the-art methods to determine the accuracy
and the entropy of the answer distributions for each cluster. A benefit of the
proposed method is that no annotation of the difficulty is required, because
the accuracy of each cluster reflects the difficulty of visual questions that
belong to it. Our approach can identify clusters of difficult visual questions
that are not answered correctly by state-of-the-art methods. Detailed analysis
on the VQA v2 dataset reveals that 1) all methods show poor performances on the
most difficult cluster (about 10\% accuracy), 2) as the cluster difficulty
increases, the answers predicted by the different methods begin to differ, and
3) the values of cluster entropy are highly correlated with the cluster
accuracy. We show that our approach has the advantage of being able to assess
the difficulty of visual questions without ground-truth (\ie, the test set of
VQA v2) by assigning them to one of the clusters. We expect that this can
stimulate the development of novel directions of research and new algorithms.

negative:
Most of the textual information available to us are temporally variable. In a
world where information is dynamic, time-stamping them is a very important
task. Documents are a good source of information and are used for many tasks
like, sentiment analysis, classification of reviews etc. The knowledge of
creation date of documents facilitates several tasks like summarization, event
extraction, temporally focused information extraction etc. Unfortunately, for
most of the documents on the web, the time-stamp meta-data is either erroneous
or missing. Thus document dating is a challenging problem which requires
inference over the temporal structure of the document alongside the contextual
information of the document. Prior document dating systems have largely relied
on handcrafted features while ignoring such document-internal structures. In
this paper we propose NeuralDater, a Graph Convolutional Network (GCN) based
document dating approach which jointly exploits syntactic and temporal graph
structures of document in a principled way. We also pointed out some
limitations of NeuralDater and tried to utilize both context and temporal
information in documents in a more flexible and intuitive manner proposing AD3:
Attentive Deep Document Dater, an attention-based document dating system. To
the best of our knowledge these are the first application of deep learning
methods for the task. Through extensive experiments on real-world datasets, we
find that our models significantly outperforms state-of-the-art baselines by a
significant margin.

negative:
Predicting pairs of anchor users plays an important role in the cross-network
analysis. Due to the expensive costs of labeling anchor users for training
prediction models, we consider in this paper the problem of minimizing the
number of user pairs across multiple networks for labeling as to improve the
accuracy of the prediction. To this end, we present a deep active learning
model for anchor user prediction (DALAUP for short). However, active learning
for anchor user sampling meets the challenges of non-i.i.d. user pair data
caused by network structures and the correlation among anchor or non-anchor
user pairs. To solve the challenges, DALAUP uses a couple of neural networks
with shared-parameter to obtain the vector representations of user pairs, and
ensembles three query strategies to select the most informative user pairs for
labeling and model training. Experiments on real-world social network data
demonstrate that DALAUP outperforms the state-of-the-art approaches.

negative:
Relating entities and events in text is a key component of natural language
understanding. Cross-document coreference resolution, in particular, is
important for the growing interest in multi-document analysis tasks. In this
work we propose a new model that extends the efficient sequential prediction
paradigm for coreference resolution to cross-document settings and achieves
competitive results for both entity and event coreference while provides strong
evidence of the efficacy of both sequential models and higher-order inference
in cross-document settings. Our model incrementally composes mentions into
cluster representations and predicts links between a mention and the already
constructed clusters, approximating a higher-order model. In addition, we
conduct extensive ablation studies that provide new insights into the
importance of various inputs and representation types in coreference.

negative:
We investigate the pertinence of methods from algebraic topology for text
data analysis. These methods enable the development of
mathematically-principled isometric-invariant mappings from a set of vectors to
a document embedding, which is stable with respect to the geometry of the
document in the selected metric space. In this work, we evaluate the utility of
these topology-based document representations in traditional NLP tasks,
specifically document clustering and sentiment classification. We find that the
embeddings do not benefit text analysis. In fact, performance is worse than
simple techniques like $\textit{tf-idf}$, indicating that the geometry of the
document does not provide enough variability for classification on the basis of
topic or sentiment in the chosen datasets.

negative:
Named entity linking is to map an ambiguous mention in documents to an entity
in a knowledge base. The named entity linking is challenging, given the fact
that there are multiple candidate entities for a mention in a document. It is
difficult to link a mention when it appears multiple times in a document, since
there are conflicts by the contexts around the appearances of the mention. In
addition, it is difficult since the given training dataset is small due to the
reason that it is done manually to link a mention to its mapping entity. In the
literature, there are many reported studies among which the recent embedding
methods learn vectors of entities from the training dataset at document level.
To address these issues, we focus on how to link entity for mentions at a
sentence level, which reduces the noises introduced by different appearances of
the same mention in a document at the expense of insufficient information to be
used. We propose a new unified embedding method by maximizing the relationships
learned from knowledge graphs. We confirm the effectiveness of our method in
our experimental studies.

negative:
Document-level relation extraction requires integrating information within
and across multiple sentences of a document and capturing complex interactions
between inter-sentence entities. However, effective aggregation of relevant
information in the document remains a challenging research question. Existing
approaches construct static document-level graphs based on syntactic trees,
co-references or heuristics from the unstructured text to model the
dependencies. Unlike previous methods that may not be able to capture rich
non-local interactions for inference, we propose a novel model that empowers
the relational reasoning across sentences by automatically inducing the latent
document-level graph. We further develop a refinement strategy, which enables
the model to incrementally aggregate relevant information for multi-hop
reasoning. Specifically, our model achieves an F1 score of 59.05 on a
large-scale document-level dataset (DocRED), significantly improving over the
previous results, and also yields new state-of-the-art results on the CDR and
GDA dataset. Furthermore, extensive analyses show that the model is able to
discover more accurate inter-sentence relations.

negative:
Sentence embedding is an important research topic in natural language
processing. It is essential to generate a good embedding vector that fully
reflects the semantic meaning of a sentence in order to achieve an enhanced
performance for various natural language processing tasks, such as machine
translation and document classification. Thus far, various sentence embedding
models have been proposed, and their feasibility has been demonstrated through
good performances on tasks following embedding, such as sentiment analysis and
sentence classification. However, because the performances of sentence
classification and sentiment analysis can be enhanced by using a simple
sentence representation method, it is not sufficient to claim that these models
fully reflect the meanings of sentences based on good performances for such
tasks. In this paper, inspired by human language recognition, we propose the
following concept of semantic coherence, which should be satisfied for a good
sentence embedding method: similar sentences should be located close to each
other in the embedding space. Then, we propose the Paraphrase-Thought
(P-thought) model to pursue semantic coherence as much as possible.
Experimental results on two paraphrase identification datasets (MS COCO and STS
benchmark) show that the P-thought models outperform the benchmarked sentence
embedding methods.

negative:
This document should serve as a quick reference for and guide to the
implementation of linear GQ($\lambda$), a gradient-based off-policy
temporal-difference learning algorithm. Explanation of the intuition and theory
behind the algorithm are provided elsewhere (e.g., Maei & Sutton 2010, Maei
2011). If you questions or concerns about the content in this document or the
attached java code please email Adam White (adam.white@ualberta.ca).
  The code is provided as part of the source files in the arXiv submission.

negative:
Recent studies emphasize the need of document context in human evaluation of
machine translations, but little research has been done on the impact of user
interfaces on annotator productivity and the reliability of assessments. In
this work, we compare human assessment data from the last two WMT evaluation
campaigns collected via two different methods for document-level evaluation.
Our analysis shows that a document-centric approach to evaluation where the
annotator is presented with the entire document context on a screen leads to
higher quality segment and document level assessments. It improves the
correlation between segment and document scores and increases inter-annotator
agreement for document scores but is considerably more time consuming for
annotators.