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 : As an education researcher, my goal is to create a system that can generate explanations for preschool children as part of their early education. I require a model that can provide real-time explanations for the problems inputted. Given that the target users are preschool children, the model needs to be capable of accepting verbal inputs, integrating phrases into natural language, and predicting the user's question. One challenge I face is that adults rarely discuss common sense and trivial questions, so I require an additional dataset to address these types of questions. It is crucial that the content in the dataset is thoroughly vetted by authorities, as the model will be used by children. The model also needs the ability to discard any inappropriate knowledge it may have previously learned. If possible, I would also like guidance on how to strike a balance between the response time of the model and the size of the dataset.

positive:
There is a growing interest in designing autonomous agents that can work
alongside humans. Such agents will undoubtedly be expected to explain their
behavior and decisions. While generating explanations is an actively researched
topic, most works tend to focus on methods that generate explanations that are
one size fits all. As in the specifics of the user-model are completely
ignored. The handful of works that look at tailoring their explanation to the
user's background rely on having specific models of the users (either analytic
models or learned labeling models). The goal of this work is thus to propose an
end-to-end adaptive explanation generation system that begins by learning the
different types of users that the agent could interact with. Then during the
interaction with the target user, it is tasked with identifying the type on the
fly and adjust its explanations accordingly. The former is achieved by a
data-driven clustering approach while for the latter, we compile our
explanation generation problem into a POMDP. We demonstrate the usefulness of
our system on two domains using state-of-the-art POMDP solvers. We also report
the results of a user study that investigates the benefits of providing
personalized explanations in a human-robot interaction setting.

positive:
Providing meaningful and actionable explanations to end-users is a
fundamental prerequisite for implementing explainable intelligent systems in
the real world. Explainability is a situated interaction between a user and the
AI system rather than being static design principles. The content of
explanations is context-dependent and must be defined by evidence about the
user and its context. This paper seeks to operationalize this concept by
proposing a formative architecture that defines the explanation space from a
user-inspired perspective. The architecture comprises five intertwined
components to outline explanation requirements for a task: (1) the end-users
mental models, (2) the end-users cognitive process, (3) the user interface, (4)
the human-explainer agent, and the (5) agent process. We first define each
component of the architecture. Then we present the Abstracted Explanation
Space, a modeling tool that aggregates the architecture's components to support
designers in systematically aligning explanations with the end-users work
practices, needs, and goals. It guides the specifications of what needs to be
explained (content - end-users mental model), why this explanation is necessary
(context - end-users cognitive process), to delimit how to explain it (format -
human-explainer agent and user interface), and when should the explanations be
given. We then exemplify the tool's use in an ongoing case study in the
aircraft maintenance domain. Finally, we discuss possible contributions of the
tool, known limitations/areas for improvement, and future work to be done.

negative:
Explainability has been a goal for Artificial Intelligence (AI) systems since
their conception, with the need for explainability growing as more complex AI
models are increasingly used in critical, high-stakes settings such as
healthcare. Explanations have often added to an AI system in a non-principled,
post-hoc manner. With greater adoption of these systems and emphasis on
user-centric explainability, there is a need for a structured representation
that treats explainability as a primary consideration, mapping end user needs
to specific explanation types and the system's AI capabilities. We design an
explanation ontology to model both the role of explanations, accounting for the
system and user attributes in the process, and the range of different
literature-derived explanation types. We indicate how the ontology can support
user requirements for explanations in the domain of healthcare. We evaluate our
ontology with a set of competency questions geared towards a system designer
who might use our ontology to decide which explanation types to include, given
a combination of users' needs and a system's capabilities, both in system
design settings and in real-time operations. Through the use of this ontology,
system designers will be able to make informed choices on which explanations AI
systems can and should provide.

negative:
Many methods now exist for conditioning model outputs on task instructions,
retrieved documents, and user-provided explanations and feedback. Rather than
relying solely on examples of task inputs and outputs, these approaches use
valuable additional data for improving model correctness and aligning learned
models with human priors. Meanwhile, a growing body of evidence suggests that
some language models can (1) store a large amount of knowledge in their
parameters, and (2) perform inference over tasks in textual inputs at test
time. These results raise the possibility that, for some tasks, humans cannot
explain to a model any more about the task than it already knows or could infer
on its own. In this paper, we study the circumstances under which explanations
of individual data points can (or cannot) improve modeling performance. In
order to carefully control important properties of the data and explanations,
we introduce a synthetic dataset for experiments, and we also make use of three
existing datasets with explanations: e-SNLI, TACRED, and SemEval. We first give
a formal framework for the available modeling approaches, in which explanation
data can be used as model inputs, as targets, or as a prior. After arguing that
the most promising role for explanation data is as model inputs, we propose to
use a retrieval-based method and show that it solves our synthetic task with
accuracies upwards of 95%, while baselines without explanation data achieve
below 65% accuracy. We then identify properties of datasets for which
retrieval-based modeling fails. With the three existing datasets, we find no
improvements from explanation retrieval. Drawing on findings from our synthetic
task, we suggest that at least one of six preconditions for successful modeling
fails to hold with these datasets. Our code is publicly available at
https://github.com/peterbhase/ExplanationRoles

negative:
Neural predictive models have achieved remarkable performance improvements in
various natural language processing tasks. However, most neural predictive
models suffer from the lack of explainability of predictions, limiting their
practical utility. This paper proposes a neural predictive approach to make a
prediction and generate its corresponding explanation simultaneously. It
leverages the knowledge entailed in explanations as an additional distillation
signal for more efficient learning. We conduct a preliminary study on Chinese
medical multiple-choice question answering, English natural language inference,
and commonsense question answering tasks. The experimental results show that
the proposed approach can generate reasonable explanations for its predictions
even with a small-scale training corpus. The proposed method also achieves
improved prediction accuracy on three datasets, which indicates that making
predictions can benefit from generating the explanation in the decision
process.

negative:
The adoption of machine learning in high-stakes applications such as
healthcare and law has lagged in part because predictions are not accompanied
by explanations comprehensible to the domain user, who often holds the ultimate
responsibility for decisions and outcomes. In this paper, we propose an
approach to generate such explanations in which training data is augmented to
include, in addition to features and labels, explanations elicited from domain
users. A joint model is then learned to produce both labels and explanations
from the input features. This simple idea ensures that explanations are
tailored to the complexity expectations and domain knowledge of the consumer.
Evaluation spans multiple modeling techniques on a game dataset, a (visual)
aesthetics dataset, a chemical odor dataset and a Melanoma dataset showing that
our approach is generalizable across domains and algorithms. Results
demonstrate that meaningful explanations can be reliably taught to machine
learning algorithms, and in some cases, also improve modeling accuracy.

negative:
Explainable machine learning and artificial intelligence models have been
used to justify a model's decision-making process. This added transparency aims
to help improve user performance and understanding of the underlying model.
However, in practice, explainable systems face many open questions and
challenges. Specifically, designers might reduce the complexity of deep
learning models in order to provide interpretability. The explanations
generated by these simplified models, however, might not accurately justify and
be truthful to the model. This can further add confusion to the users as they
might not find the explanations meaningful with respect to the model
predictions. Understanding how these explanations affect user behavior is an
ongoing challenge. In this paper, we explore how explanation veracity affects
user performance and agreement in intelligent systems. Through a controlled
user study with an explainable activity recognition system, we compare
variations in explanation veracity for a video review and querying task. The
results suggest that low veracity explanations significantly decrease user
performance and agreement compared to both accurate explanations and a system
without explanations. These findings demonstrate the importance of accurate and
understandable explanations and caution that poor explanations can sometimes be
worse than no explanations with respect to their effect on user performance and
reliance on an AI system.

negative:
With the increasing complexity of CPSs, their behavior and decisions become
increasingly difficult to understand and comprehend for users and other
stakeholders. Our vision is to build self-explainable systems that can, at
run-time, answer questions about the system's past, current, and future
behavior. As hitherto no design methodology or reference framework exists for
building such systems, we propose the MAB-EX framework for building
self-explainable systems that leverage requirements- and explainability models
at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain
behavior of a system, then Build an explanation from explanation models and
convey this EXplanation in a suitable way to a stakeholder. We also take into
account that new explanations can be learned, by updating the explanation
models, should new and yet un-explainable behavior be detected by the system.

negative:
We propose a new method for generating explanations with AI and a tool to
test its expressive power within a user interface. In order to bridge the gap
between philosophy and human-computer interfaces, we show a new approach for
the generation of interactive explanations based on a sophisticated pipeline of
AI algorithms for structuring natural language documents into knowledge graphs,
answering questions effectively and satisfactorily. With this work we aim to
prove that the philosophical theory of explanations presented by Achinstein can
be actually adapted for being implemented into a concrete software application,
as an interactive and illocutionary process of answering questions.
Specifically, our contribution is an approach to frame illocution in a
computer-friendly way, to achieve user-centrality with statistical question
answering. Indeed, we frame the illocution of an explanatory process as that
mechanism responsible for anticipating the needs of the explainee in the form
of unposed, implicit, archetypal questions, hence improving the user-centrality
of the underlying explanatory process. Therefore, we hypothesise that if an
explanatory process is an illocutionary act of providing content-giving answers
to questions, and illocution is as we defined it, the more explicit and
implicit questions can be answered by an explanatory tool, the more usable its
explanations. We tested our hypothesis with a user-study involving more than 60
participants, on two XAI-based systems, one for credit approval and one for
heart disease prediction. The results showed that increasing the illocutionary
power of an explanatory tool can produce statistically significant improvements
on effectiveness. This, combined with a visible alignment between the
increments in effectiveness and satisfaction, suggests that our understanding
of illocution can be correct, giving evidence in favour of our theory.

negative:
As Graph Neural Networks (GNNs) are widely adopted in digital pathology,
there is increasing attention to developing explanation models (explainers) of
GNNs for improved transparency in clinical decisions.
  Existing explainers discover an explanatory subgraph relevant to the
prediction.
  However, such a subgraph is insufficient to reveal all the critical
biological substructures for the prediction because the prediction will remain
unchanged after removing that subgraph.
  Hence, an explanatory subgraph should be not only necessary for prediction,
but also sufficient to uncover the most predictive regions for the explanation.
  Such explanation requires a measurement of information transferred from
different input subgraphs to the predictive output, which we define as
information flow.
  In this work, we address these key challenges and propose IFEXPLAINER, which
generates a necessary and sufficient explanation for GNNs.
  To evaluate the information flow within GNN's prediction, we first propose a
novel notion of predictiveness, named $f$-information, which is directional and
incorporates the realistic capacity of the GNN model.
  Based on it, IFEXPLAINER generates the explanatory subgraph with maximal
information flow to the prediction.
  Meanwhile, it minimizes the information flow from the input to the predictive
result after removing the explanation.
  Thus, the produced explanation is necessarily important to the prediction and
sufficient to reveal the most crucial substructures.
  We evaluate IFEXPLAINER to interpret GNN's predictions on breast cancer
subtyping.
  Experimental results on the BRACS dataset show the superior performance of
the proposed method.

negative:
Counterfactual explanation methods interpret the outputs of a machine
learning model in the form of "what-if scenarios" without compromising the
fidelity-interpretability trade-off. They explain how to obtain a desired
prediction from the model by recommending small changes to the input features,
aka recourse. We believe an actionable recourse should be created based on
sound counterfactual explanations originating from the distribution of the
ground-truth data and linked to the domain knowledge. Moreover, it needs to
preserve the coherency between changed/unchanged features while satisfying
user/domain-specified constraints. This paper introduces CARE, a modular
explanation framework that addresses the model- and user-level desiderata in a
consecutive and structured manner. We tackle the existing requirements by
proposing novel and efficient solutions that are formulated in a
multi-objective optimization framework. The designed framework enables
including arbitrary requirements and generating counterfactual explanations and
actionable recourse by choice. As a model-agnostic approach, CARE generates
multiple, diverse explanations for any black-box model in tabular
classification and regression settings. Several experiments on standard data
sets and black-box models demonstrate the effectiveness of our modular
framework and its superior performance compared to the baselines.

negative:
An accurate abstractive summary of a document should contain all its salient
information and should be logically entailed by the input document. We improve
these important aspects of abstractive summarization via multi-task learning
with the auxiliary tasks of question generation and entailment generation,
where the former teaches the summarization model how to look for salient
questioning-worthy details, and the latter teaches the model how to rewrite a
summary which is a directed-logical subset of the input document. We also
propose novel multi-task architectures with high-level (semantic)
layer-specific sharing across multiple encoder and decoder layers of the three
tasks, as well as soft-sharing mechanisms (and show performance ablations and
analysis examples of each contribution). Overall, we achieve statistically
significant improvements over the state-of-the-art on both the CNN/DailyMail
and Gigaword datasets, as well as on the DUC-2002 transfer setup. We also
present several quantitative and qualitative analysis studies of our model's
learned saliency and entailment skills.

negative:
A central issue addressed by the rapidly growing research area of eXplainable
Artificial Intelligence (XAI) is to provide methods to give explanations for
the behaviours of Machine Learning (ML) non-interpretable models after the
training. Recently, it is becoming more and more evident that new directions to
create better explanations should take into account what a good explanation is
to a human user. This paper suggests taking advantage of developing an XAI
framework that allows producing multiple explanations for the response of image
a classification system in terms of potentially different middle-level input
features. To this end, we propose an XAI framework able to construct
explanations in terms of input features extracted by auto-encoders. We start
from the hypothesis that some autoencoders, relying on standard data
representation approaches, could extract more salient and understandable input
properties, which we call here \textit{Middle-Level input Features} (MLFs), for
a user with respect to raw low-level features. Furthermore, extracting
different types of MLFs through different type of autoencoders, different types
of explanations for the same ML system behaviour can be returned. We
experimentally tested our method on two different image datasets and using
three different types of MLFs. The results are encouraging. Although our novel
approach was tested in the context of image classification, it can potentially
be used on other data types to the extent that auto-encoders to extract humanly
understandable representations can be applied.

negative:
Developing methods of automated inference that are able to provide users with
compelling human-readable justifications for why the answer to a question is
correct is critical for domains such as science and medicine, where user trust
and detecting costly errors are limiting factors to adoption. One of the
central barriers to training question answering models on explainable inference
tasks is the lack of gold explanations to serve as training data. In this paper
we present a corpus of explanations for standardized science exams, a recent
challenge task for question answering. We manually construct a corpus of
detailed explanations for nearly all publicly available standardized elementary
science question (approximately 1,680 3rd through 5th grade questions) and
represent these as "explanation graphs" -- sets of lexically overlapping
sentences that describe how to arrive at the correct answer to a question
through a combination of domain and world knowledge. We also provide an
explanation-centered tablestore, a collection of semi-structured tables that
contain the knowledge to construct these elementary science explanations.
Together, these two knowledge resources map out a substantial portion of the
knowledge required for answering and explaining elementary science exams, and
provide both structured and free-text training data for the explainable
inference task.

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 desirable property of learning systems is to be both effective and
interpretable. Towards this goal, recent models have been proposed that first
generate an extractive explanation from the input text and then generate a
prediction on just the explanation called explain-then-predict models. These
models primarily consider the task input as a supervision signal in learning an
extractive explanation and do not effectively integrate rationales data as an
additional inductive bias to improve task performance. We propose a novel yet
simple approach ExPred, that uses multi-task learning in the explanation
generation phase effectively trading-off explanation and prediction losses. And
then we use another prediction network on just the extracted explanations for
optimizing the task performance. We conduct an extensive evaluation of our
approach on three diverse language datasets -- fact verification, sentiment
classification, and QA -- and find that we substantially outperform existing
approaches.

negative:
Deep neural networks are becoming more and more popular due to their
revolutionary success in diverse areas, such as computer vision, natural
language processing, and speech recognition. However, the decision-making
processes of these models are generally not interpretable to users. In various
domains, such as healthcare, finance, or law, it is critical to know the
reasons behind a decision made by an artificial intelligence system. Therefore,
several directions for explaining neural models have recently been explored. In
this thesis, I investigate two major directions for explaining deep neural
networks. The first direction consists of feature-based post-hoc explanatory
methods, that is, methods that aim to explain an already trained and fixed
model (post-hoc), and that provide explanations in terms of input features,
such as tokens for text and superpixels for images (feature-based). The second
direction consists of self-explanatory neural models that generate natural
language explanations, that is, models that have a built-in module that
generates explanations for the predictions of the model.

negative:
We explore the problem of step-wise explaining how to solve constraint
satisfaction problems, with a use case on logic grid puzzles. More
specifically, we study the problem of explaining the inference steps that one
can take during propagation, in a way that is easy to interpret for a person.
Thereby, we aim to give the constraint solver explainable agency, which can
help in building trust in the solver by being able to understand and even learn
from the explanations. The main challenge is that of finding a sequence of
simple explanations, where each explanation should aim to be as cognitively
easy as possible for a human to verify and understand. This contrasts with the
arbitrary combination of facts and constraints that the solver may use when
propagating. We propose the use of a cost function to quantify how simple an
individual explanation of an inference step is, and identify the
explanation-production problem of finding the best sequence of explanations of
a CSP. Our approach is agnostic of the underlying constraint propagation
mechanisms, and can provide explanations even for inference steps resulting
from combinations of constraints. In case multiple constraints are involved, we
also develop a mechanism that allows to break the most difficult steps up and
thus gives the user the ability to zoom in on specific parts of the
explanation. Our proposed algorithm iteratively constructs the explanation
sequence by using an optimistic estimate of the cost function to guide the
search for the best explanation at each step. Our experiments on logic grid
puzzles show the feasibility of the approach in terms of the quality of the
individual explanations and the resulting explanation sequences obtained.

negative:
As machine learning models become more accurate, they typically become more
complex and uninterpretable by humans. The black-box character of these models
holds back its acceptance in practice, especially in high-risk domains where
the consequences of failure could be catastrophic such as health-care or
defense. Providing understandable and useful explanations behind ML models or
predictions can increase the trust of the user. Example-based reasoning, which
entails leveraging previous experience with analogous tasks to make a decision,
is a well known strategy for problem solving and justification. This work
presents a new explanation extraction method called LEAFAGE, for a prediction
made by any black-box ML model. The explanation consists of the visualization
of similar examples from the training set and the importance of each feature.
Moreover, these explanations are contrastive which aims to take the
expectations of the user into account. LEAFAGE is evaluated in terms of
fidelity to the underlying black-box model and usefulness to the user. The
results showed that LEAFAGE performs overall better than the current
state-of-the-art method LIME in terms of fidelity, on ML models with non-linear
decision boundary. A user-study was conducted which focused on revealing the
differences between example-based and feature importance-based explanations. It
showed that example-based explanations performed significantly better than
feature importance-based explanation, in terms of perceived transparency,
information sufficiency, competence and confidence. Counter-intuitively, when
the gained knowledge of the participants was tested, it showed that they
learned less about the black-box model after seeing a feature importance-based
explanation than seeing no explanation at all. The participants found feature
importance-based explanation vague and hard to generalize it to other
instances.

negative:
Prior work on generating explanations in a planning and decision-making
context has focused on providing the rationale behind an AI agent's decision
making. While these methods provide the right explanations from the explainer's
perspective, they fail to heed the cognitive requirement of understanding an
explanation from the explainee's (the human's) perspective. In this work, we
set out to address this issue by first considering the influence of information
order in an explanation, or the progressiveness of explanations. Intuitively,
progression builds later concepts on previous ones and is known to contribute
to better learning. In this work, we aim to investigate similar effects during
explanation generation when an explanation is broken into multiple parts that
are communicated sequentially. The challenge here lies in modeling the humans'
preferences for information order in receiving such explanations to assist
understanding. Given this sequential process, a formulation based on goal-based
MDP for generating progressive explanations is presented. The reward function
of this MDP is learned via inverse reinforcement learning based on explanations
that are retrieved via human subject studies. We first evaluated our approach
on a scavenger-hunt domain to demonstrate its effectively in capturing the
humans' preferences. Upon analyzing the results, it revealed something more
fundamental: the preferences arise strongly from both domain dependent and
independence features. The correlation with domain independent features pushed
us to verify this result further in an escape room domain. Results confirmed
our hypothesis that the process of understanding an explanation was a dynamic
process. The human preference that reflected this aspect corresponded exactly
to the progression for knowledge assimilation hidden deeper in our cognitive
process.

negative:
Recently, there has been increasing interest in transparency and
interpretability in Deep Reinforcement Learning (DRL) systems. Verbal
explanations, as the most natural way of communication in our daily life,
deserve more attention, since they allow users to gain a better understanding
of the system which ultimately could lead to a high level of trust and smooth
collaboration. This paper reports a novel work in generating verbal
explanations for DRL behaviors agent. A rule-based model is designed to
construct explanations using a series of rules which are predefined with prior
knowledge. A learning model is then proposed to expand the implicit logic of
generating verbal explanation to general situations by employing rule-based
explanations as training data. The learning model is shown to have better
flexibility and generalizability than the static rule-based model. The
performance of both models is evaluated quantitatively through objective
metrics. The results show that verbal explanation generated by both models
improve subjective satisfaction of users towards the interpretability of DRL
systems. Additionally, seven variants of the learning model are designed to
illustrate the contribution of input channels, attention mechanism, and
proposed encoder in improving the quality of verbal explanation.

negative:
Local Interpretable Model-Agnostic Explanations (LIME) is a popular method to
perform interpretability of any kind of Machine Learning (ML) model. It
explains one ML prediction at a time, by learning a simple linear model around
the prediction. The model is trained on randomly generated data points, sampled
from the training dataset distribution and weighted according to the distance
from the reference point - the one being explained by LIME. Feature selection
is applied to keep only the most important variables. LIME is widespread across
different domains, although its instability - a single prediction may obtain
different explanations - is one of the major shortcomings. This is due to the
randomness in the sampling step, as well as to the flexibility in tuning the
weights and determines a lack of reliability in the retrieved explanations,
making LIME adoption problematic. In Medicine especially, clinical
professionals trust is mandatory to determine the acceptance of an explainable
algorithm, considering the importance of the decisions at stake and the related
legal issues. In this paper, we highlight a trade-off between explanation's
stability and adherence, namely how much it resembles the ML model. Exploiting
our innovative discovery, we propose a framework to maximise stability, while
retaining a predefined level of adherence. OptiLIME provides freedom to choose
the best adherence-stability trade-off level and more importantly, it clearly
highlights the mathematical properties of the retrieved explanation. As a
result, the practitioner is provided with tools to decide whether the
explanation is reliable, according to the problem at hand. We extensively test
OptiLIME on a toy dataset - to present visually the geometrical findings - and
a medical dataset. In the latter, we show how the method comes up with
meaningful explanations both from a medical and mathematical standpoint.

negative:
The need for transparency of predictive systems based on Machine Learning
algorithms arises as a consequence of their ever-increasing proliferation in
the industry. Whenever black-box algorithmic predictions influence human
affairs, the inner workings of these algorithms should be scrutinised and their
decisions explained to the relevant stakeholders, including the system
engineers, the system's operators and the individuals whose case is being
decided. While a variety of interpretability and explainability methods is
available, none of them is a panacea that can satisfy all diverse expectations
and competing objectives that might be required by the parties involved. We
address this challenge in this paper by discussing the promises of Interactive
Machine Learning for improved transparency of black-box systems using the
example of contrastive explanations -- a state-of-the-art approach to
Interpretable Machine Learning.
  Specifically, we show how to personalise counterfactual explanations by
interactively adjusting their conditional statements and extract additional
explanations by asking follow-up "What if?" questions. Our experience in
building, deploying and presenting this type of system allowed us to list
desired properties as well as potential limitations, which can be used to guide
the development of interactive explainers. While customising the medium of
interaction, i.e., the user interface comprising of various communication
channels, may give an impression of personalisation, we argue that adjusting
the explanation itself and its content is more important. To this end,
properties such as breadth, scope, context, purpose and target of the
explanation have to be considered, in addition to explicitly informing the
explainee about its limitations and caveats...

negative:
The paper presents an approach to build a question and answer system that is
capable of processing the information in a large dataset and allows the user to
gain knowledge from this dataset by asking questions in natural language form.
Key content of this research covers four dimensions which are; Corpus
Preprocessing, Question Preprocessing, Deep Neural Network for Answer
Extraction and Answer Generation. The system is capable of understanding the
question, responds to the user's query in natural language form as well. The
goal is to make the user feel as if they were interacting with a person than a
machine.

negative:
Feature based local attribution methods are amongst the most prevalent in
explainable artificial intelligence (XAI) literature. Going beyond standard
correlation, recently, methods have been proposed that highlight what should be
minimally sufficient to justify the classification of an input (viz. pertinent
positives). While minimal sufficiency is an attractive property, the resulting
explanations are often too sparse for a human to understand and evaluate the
local behavior of the model, thus making it difficult to judge its overall
quality. To overcome these limitations, we propose a novel method called
Path-Sufficient Explanations Method (PSEM) that outputs a sequence of
sufficient explanations for a given input of strictly decreasing size (or
value) -- from original input to a minimally sufficient explanation -- which
can be thought to trace the local boundary of the model in a smooth manner,
thus providing better intuition about the local model behavior for the specific
input. We validate these claims, both qualitatively and quantitatively, with
experiments that show the benefit of PSEM across all three modalities (image,
tabular and text). A user study depicts the strength of the method in
communicating the local behavior, where (many) users are able to correctly
determine the prediction made by a model.

negative:
When answering a question, people often draw upon their rich world knowledge
in addition to the particular context. Recent work has focused primarily on
answering questions given some relevant document or context, and required very
little general background. To investigate question answering with prior
knowledge, we present CommonsenseQA: a challenging new dataset for commonsense
question answering. To capture common sense beyond associations, we extract
from ConceptNet (Speer et al., 2017) multiple target concepts that have the
same semantic relation to a single source concept. Crowd-workers are asked to
author multiple-choice questions that mention the source concept and
discriminate in turn between each of the target concepts. This encourages
workers to create questions with complex semantics that often require prior
knowledge. We create 12,247 questions through this procedure and demonstrate
the difficulty of our task with a large number of strong baselines. Our best
baseline is based on BERT-large (Devlin et al., 2018) and obtains 56% accuracy,
well below human performance, which is 89%.

negative:
In consequential domains such as recidivism prediction, facility inspection,
and benefit assignment, it's important for individuals to know the
decision-relevant information for the model's prediction. In addition,
predictions should be fair both in terms of the outcome and the justification
of the outcome. In other words, decision-relevant features should provide
sufficient information for the predicted outcome and should be independent of
the membership of individuals in protected groups such as race and gender. In
this work, we focus on the problem of (un)fairness in the justification of the
text-based neural models. We tie the explanatory power of the model to fairness
in the outcome and propose a fairness-aware summarization mechanism to detect
and counteract the bias in such models. Given a potentially biased natural
language explanation for a decision, we use a multi-task neural model and an
attribution mechanism based on integrated gradients to extract high-utility and
low-bias justifications in form of a summary. The extracted summary is then
used for training a model to make decisions for individuals. Results on several
real world datasets suggest that our method drastically limits the demographic
leakage in the input (fairness in justification) while moderately enhancing the
fairness in the outcome. Our model is also effective in detecting and
counteracting several types of data poisoning attacks that synthesize
race-coded reasoning or irrelevant justifications.

negative:
The use of deep neural networks to make high risk decisions creates a need
for global and local explanations so that users and experts have confidence in
the modeling algorithms. We introduce a novel technique to find global and
local explanations for time series data used in binary classification machine
learning systems. We identify the most salient of the original features used by
a black box model to distinguish between classes. The explanation can be made
on categorical, continuous, and time series data and can be generalized to any
binary classification model. The analysis is conducted on time series data to
train a long short-term memory deep neural network and uses the time dependent
structure of the underlying features in the explanation. The proposed technique
attributes weights to features to explain an observations risk of belonging to
a class as a multiplicative factor of a base hazard rate. We use a variation of
the Cox Proportional Hazards regression, a Generalized Additive Model, to
explain the effect of variables upon the probability of an in-class response
for a score output from the black box model. The covariates incorporate time
dependence structure in the features so the explanation is inclusive of the
underlying time series data structure.

negative:
As AI becomes an integral part of our lives, the development of explainable
AI, embodied in the decision-making process of an AI or robotic agent, becomes
imperative. For a robotic teammate, the ability to generate explanations to
justify its behavior is one of the key requirements of explainable agency.
Prior work on explanation generation has been focused on supporting the
rationale behind the robot's decision or behavior. These approaches, however,
fail to consider the mental demand for understanding the received explanation.
In other words, the human teammate is expected to understand an explanation no
matter how much information is presented. In this work, we argue that
explanations, especially those of a complex nature, should be made in an online
fashion during the execution, which helps spread out the information to be
explained and thus reduce the mental workload of humans in highly cognitive
demanding tasks. However, a challenge here is that the different parts of an
explanation may be dependent on each other, which must be taken into account
when generating online explanations. To this end, a general formulation of
online explanation generation is presented with three variations satisfying
different "online" properties. The new explanation generation methods are based
on a model reconciliation setting introduced in our prior work. We evaluated
our methods both with human subjects in a simulated rover domain, using NASA
Task Load Index (TLX), and synthetically with ten different problems across two
standard IPC domains. Results strongly suggest that our methods generate
explanations that are perceived as less cognitively demanding and much
preferred over the baselines and are computationally efficient.

negative:
Machine learning plays an increasing role in intelligent tutoring systems as
both the amount of data available and specialization among students grow.
Nowadays, these systems are frequently deployed on mobile applications. Users
on such mobile education platforms are dynamic, frequently being added,
accessing the application with varying levels of focus, and changing while
using the service. The education material itself, on the other hand, is often
static and is an exhaustible resource whose use in tasks such as problem
recommendation must be optimized. The ability to update user models with
respect to educational material in real-time is thus essential; however,
existing approaches require time-consuming re-training of user features
whenever new data is added. In this paper, we introduce a neural pedagogical
agent for real-time user modeling in the task of predicting user response
correctness, a central task for mobile education applications. Our model,
inspired by work in natural language processing on sequence modeling and
machine translation, updates user features in real-time via bidirectional
recurrent neural networks with an attention mechanism over embedded
question-response pairs. We experiment on the mobile education application
SantaTOEIC, which has 559k users, 66M response data points as well as a set of
10k study problems each expert-annotated with topic tags and gathered since
2016. Our model outperforms existing approaches over several metrics in
predicting user response correctness, notably out-performing other methods on
new users without large question-response histories. Additionally, our
attention mechanism and annotated tag set allow us to create an interpretable
education platform, with a smart review system that addresses the
aforementioned issue of varied user attention and problem exhaustion.

negative:
Building a dialogue system that can communicate naturally with humans is a
challenging yet interesting problem of agent-based computing. The rapid growth
in this area is usually hindered by the long-standing problem of data scarcity
as these systems are expected to learn syntax, grammar, decision making, and
reasoning from insufficient amounts of task-specific dataset. The recently
introduced pre-trained language models have the potential to address the issue
of data scarcity and bring considerable advantages by generating contextualized
word embeddings. These models are considered counterpart of ImageNet in NLP and
have demonstrated to capture different facets of language such as hierarchical
relations, long-term dependency, and sentiment. In this short survey paper, we
discuss the recent progress made in the field of pre-trained language models.
We also deliberate that how the strengths of these language models can be
leveraged in designing more engaging and more eloquent conversational agents.
This paper, therefore, intends to establish whether these pre-trained models
can overcome the challenges pertinent to dialogue systems, and how their
architecture could be exploited in order to overcome these challenges. Open
challenges in the field of dialogue systems have also been deliberated.

negative:
Explainable question answering systems predict an answer together with an
explanation showing why the answer has been selected. The goal is to enable
users to assess the correctness of the system and understand its reasoning
process. However, we show that current models and evaluation settings have
shortcomings regarding the coupling of answer and explanation which might cause
serious issues in user experience. As a remedy, we propose a hierarchical model
and a new regularization term to strengthen the answer-explanation coupling as
well as two evaluation scores to quantify the coupling. We conduct experiments
on the HOTPOTQA benchmark data set and perform a user study. The user study
shows that our models increase the ability of the users to judge the
correctness of the system and that scores like F1 are not enough to estimate
the usefulness of a model in a practical setting with human users. Our scores
are better aligned with user experience, making them promising candidates for
model selection.

negative:
The study of adverse childhood experiences and their consequences has emerged
over the past 20 years. In this study, we aimed to leverage explainable
artificial intelligence, and propose a proof-of-concept prototype for a
knowledge-driven evidence-based recommendation system to improve surveillance
of adverse childhood experiences. We used concepts from an ontology that we
have developed to build and train a question-answering agent using the Google
DialogFlow engine. In addition to the question-answering agent, the initial
prototype includes knowledge graph generation and recommendation components
that leverage third-party graph technology. To showcase the framework
functionalities, we here present a prototype design and demonstrate the main
features through four use case scenarios motivated by an initiative currently
implemented at a children hospital in Memphis, Tennessee. Ongoing development
of the prototype requires implementing an optimization algorithm of the
recommendations, incorporating a privacy layer through a personal health
library, and conducting a clinical trial to assess both usability and
usefulness of the implementation. This semantic-driven explainable artificial
intelligence prototype can enhance health care practitioners ability to provide
explanations for the decisions they make.

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:
In this paper we propose a novel method that provides contrastive
explanations justifying the classification of an input by a black box
classifier such as a deep neural network. Given an input we find what should be
%necessarily and minimally and sufficiently present (viz. important object
pixels in an image) to justify its classification and analogously what should
be minimally and necessarily \emph{absent} (viz. certain background pixels). We
argue that such explanations are natural for humans and are used commonly in
domains such as health care and criminology. What is minimally but critically
\emph{absent} is an important part of an explanation, which to the best of our
knowledge, has not been explicitly identified by current explanation methods
that explain predictions of neural networks. We validate our approach on three
real datasets obtained from diverse domains; namely, a handwritten digits
dataset MNIST, a large procurement fraud dataset and a brain activity strength
dataset. In all three cases, we witness the power of our approach in generating
precise explanations that are also easy for human experts to understand and
evaluate.

negative:
Providing explanations is considered an imperative ability for an AI agent in
a human-robot teaming framework. The right explanation provides the rationale
behind an AI agent's decision-making. However, to maintain the human teammate's
cognitive demand to comprehend the provided explanations, prior works have
focused on providing explanations in a specific order or intertwining the
explanation generation with plan execution. Moreover, these approaches do not
consider the degree of details required to share throughout the provided
explanations. In this work, we argue that the agent-generated explanations,
especially the complex ones, should be abstracted to be aligned with the level
of details the human teammate desires to maintain the recipient's cognitive
load. Therefore, learning a hierarchical explanations model is a challenging
task. Moreover, the agent needs to follow a consistent high-level policy to
transfer the learned teammate preferences to a new scenario while lower-level
detailed plans are different. Our evaluation confirmed the process of
understanding an explanation, especially a complex and detailed explanation, is
hierarchical. The human preference that reflected this aspect corresponded
exactly to creating and employing abstraction for knowledge assimilation hidden
deeper in our cognitive process. We showed that hierarchical explanations
achieved better task performance and behavior interpretability while reduced
cognitive load. These results shed light on designing explainable agents
utilizing reinforcement learning and planning across various domains.

negative:
To generate trust with their users, Explainable Artificial Intelligence (XAI)
systems need to include an explanation model that can communicate the internal
decisions, behaviours and actions to the interacting humans. Successful
explanation involves both cognitive and social processes. In this paper we
focus on the challenge of meaningful interaction between an explainer and an
explainee and investigate the structural aspects of an explanation in order to
propose a human explanation dialog model. We follow a bottom-up approach to
derive the model by analysing transcripts of 398 different explanation dialog
types. We use grounded theory to code and identify key components of which an
explanation dialog consists. We carry out further analysis to identify the
relationships between components and sequences and cycles that occur in a
dialog. We present a generalized state model obtained by the analysis and
compare it with an existing conceptual dialog model of explanation.

negative:
Our goal is to create an interactive natural language interface that
efficiently and reliably learns from users to complete tasks in simulated
robotics settings. We introduce a neural semantic parsing system that learns
new high-level abstractions through decomposition: users interactively teach
the system by breaking down high-level utterances describing novel behavior
into low-level steps that it can understand. Unfortunately, existing methods
either rely on grammars which parse sentences with limited flexibility, or
neural sequence-to-sequence models that do not learn efficiently or reliably
from individual examples. Our approach bridges this gap, demonstrating the
flexibility of modern neural systems, as well as the one-shot reliable
generalization of grammar-based methods. Our crowdsourced interactive
experiments suggest that over time, users complete complex tasks more
efficiently while using our system by leveraging what they just taught. At the
same time, getting users to trust the system enough to be incentivized to teach
high-level utterances is still an ongoing challenge. We end with a discussion
of some of the obstacles we need to overcome to fully realize the potential of
the interactive paradigm.

negative:
A long-term goal of machine learning research is to build an intelligent
dialog agent. Most research in natural language understanding has focused on
learning from fixed training sets of labeled data, with supervision either at
the word level (tagging, parsing tasks) or sentence level (question answering,
machine translation). This kind of supervision is not realistic of how humans
learn, where language is both learned by, and used for, communication. In this
work, we study dialog-based language learning, where supervision is given
naturally and implicitly in the response of the dialog partner during the
conversation. We study this setup in two domains: the bAbI dataset of (Weston
et al., 2015) and large-scale question answering from (Dodge et al., 2015). We
evaluate a set of baseline learning strategies on these tasks, and show that a
novel model incorporating predictive lookahead is a promising approach for
learning from a teacher's response. In particular, a surprising result is that
it can learn to answer questions correctly without any reward-based supervision
at all.

negative:
Commonsense inference to understand and explain human language is a
fundamental research problem in natural language processing. Explaining human
conversations poses a great challenge as it requires contextual understanding,
planning, inference, and several aspects of reasoning including causal,
temporal, and commonsense reasoning. In this work, we introduce CIDER -- a
manually curated dataset that contains dyadic dialogue explanations in the form
of implicit and explicit knowledge triplets inferred using contextual
commonsense inference. Extracting such rich explanations from conversations can
be conducive to improving several downstream applications. The annotated
triplets are categorized by the type of commonsense knowledge present (e.g.,
causal, conditional, temporal). We set up three different tasks conditioned on
the annotated dataset: Dialogue-level Natural Language Inference, Span
Extraction, and Multi-choice Span Selection. Baseline results obtained with
transformer-based models reveal that the tasks are difficult, paving the way
for promising future research. The dataset and the baseline implementations are
publicly available at https://cider-task.github.io/cider/.

negative:
Current conversational AI systems aim to understand a set of pre-designed
requests and execute related actions, which limits them to evolve naturally and
adapt based on human interactions. Motivated by how children learn their first
language interacting with adults, this paper describes a new Teachable AI
system that is capable of learning new language nuggets called concepts,
directly from end users using live interactive teaching sessions. The proposed
setup uses three models to: a) Identify gaps in understanding automatically
during live conversational interactions, b) Learn the respective
interpretations of such unknown concepts from live interactions with users, and
c) Manage a classroom sub-dialogue specifically tailored for interactive
teaching sessions. We propose state-of-the-art transformer based neural
architectures of models, fine-tuned on top of pre-trained models, and show
accuracy improvements on the respective components. We demonstrate that this
method is very promising in leading way to build more adaptive and personalized
language understanding models.

negative:
When experiencing an information need, users want to engage with a domain
expert, but often turn to an information retrieval system, such as a search
engine, instead. Classical information retrieval systems do not answer
information needs directly, but instead provide references to (hopefully
authoritative) answers. Successful question answering systems offer a limited
corpus created on-demand by human experts, which is neither timely nor
scalable. Pre-trained language models, by contrast, are capable of directly
generating prose that may be responsive to an information need, but at present
they are dilettantes rather than domain experts -- they do not have a true
understanding of the world, they are prone to hallucinating, and crucially they
are incapable of justifying their utterances by referring to supporting
documents in the corpus they were trained over. This paper examines how ideas
from classical information retrieval and pre-trained language models can be
synthesized and evolved into systems that truly deliver on the promise of
domain expert advice.

negative:
The encoder-decoder framework achieves state-of-the-art results in keyphrase
generation (KG) tasks by predicting both present keyphrases that appear in the
source document and absent keyphrases that do not. However, relying solely on
the source document can result in generating uncontrollable and inaccurate
absent keyphrases. To address these problems, we propose a novel graph-based
method that can capture explicit knowledge from related references. Our model
first retrieves some document-keyphrases pairs similar to the source document
from a pre-defined index as references. Then a heterogeneous graph is
constructed to capture relationships of different granularities between the
source document and its references. To guide the decoding process, a
hierarchical attention and copy mechanism is introduced, which directly copies
appropriate words from both the source document and its references based on
their relevance and significance. The experimental results on multiple KG
benchmarks show that the proposed model achieves significant improvements
against other baseline models, especially with regard to the absent keyphrase
prediction.

negative:
Visual reasoning with compositional natural language instructions, e.g.,
based on the newly-released Cornell Natural Language Visual Reasoning (NLVR)
dataset, is a challenging task, where the model needs to have the ability to
create an accurate mapping between the diverse phrases and the several objects
placed in complex arrangements in the image. Further, this mapping needs to be
processed to answer the question in the statement given the ordering and
relationship of the objects across three similar images. In this paper, we
propose a novel end-to-end neural model for the NLVR task, where we first use
joint bidirectional attention to build a two-way conditioning between the
visual information and the language phrases. Next, we use an RL-based pointer
network to sort and process the varying number of unordered objects (so as to
match the order of the statement phrases) in each of the three images and then
pool over the three decisions. Our model achieves strong improvements (of 4-6%
absolute) over the state-of-the-art on both the structured representation and
raw image versions of the dataset.

negative:
With the ever-increasing popularity and applications of graph neural
networks, several proposals have been made to explain and understand the
decisions of a graph neural network. Explanations for graph neural networks
differ in principle from other input settings. It is important to attribute the
decision to input features and other related instances connected by the graph
structure. We find that the previous explanation generation approaches that
maximize the mutual information between the label distribution produced by the
model and the explanation to be restrictive. Specifically, existing approaches
do not enforce explanations to be valid, sparse, or robust to input
perturbations. In this paper, we lay down some of the fundamental principles
that an explanation method for graph neural networks should follow and
introduce a metric RDT-Fidelity as a measure of the explanation's
effectiveness. We propose a novel approach Zorro based on the principles from
rate-distortion theory that uses a simple combinatorial procedure to optimize
for RDT-Fidelity. Extensive experiments on real and synthetic datasets reveal
that Zorro produces sparser, stable, and more faithful explanations than
existing graph neural network explanation approaches.

negative:
Recent approaches in literature have exploited the multi-modal information in
documents (text, layout, image) to serve specific downstream document tasks.
However, they are limited by their - (i) inability to learn cross-modal
representations across text, layout and image dimensions for documents and (ii)
inability to process multi-page documents. Pre-training techniques have been
shown in Natural Language Processing (NLP) domain to learn generic textual
representations from large unlabelled datasets, applicable to various
downstream NLP tasks. In this paper, we propose a multi-task learning-based
framework that utilizes a combination of self-supervised and supervised
pre-training tasks to learn a generic document representation applicable to
various downstream document tasks. Specifically, we introduce Document Topic
Modelling and Document Shuffle Prediction as novel pre-training tasks to learn
rich image representations along with the text and layout representations for
documents. We utilize the Longformer network architecture as the backbone to
encode the multi-modal information from multi-page documents in an end-to-end
fashion. We showcase the applicability of our pre-training framework on a
variety of different real-world document tasks such as document classification,
document information extraction, and document retrieval. We evaluate our
framework on different standard document datasets and conduct exhaustive
experiments to compare performance against various ablations of our framework
and state-of-the-art baselines.

negative:
Major advances have recently been made in merging language and vision
representations. But most tasks considered so far have confined themselves to
the processing of objects and lexicalised relations amongst objects (content
words). We know, however, that humans (even pre-school children) can abstract
over raw data to perform certain types of higher-level reasoning, expressed in
natural language by function words. A case in point is given by their ability
to learn quantifiers, i.e. expressions like 'few', 'some' and 'all'. From
formal semantics and cognitive linguistics, we know that quantifiers are
relations over sets which, as a simplification, we can see as proportions. For
instance, in 'most fish are red', most encodes the proportion of fish which are
red fish. In this paper, we study how well current language and vision
strategies model such relations. We show that state-of-the-art attention
mechanisms coupled with a traditional linguistic formalisation of quantifiers
gives best performance on the task. Additionally, we provide insights on the
role of 'gist' representations in quantification. A 'logical' strategy to
tackle the task would be to first obtain a numerosity estimation for the two
involved sets and then compare their cardinalities. We however argue that
precisely identifying the composition of the sets is not only beyond current
state-of-the-art models but perhaps even detrimental to a task that is most
efficiently performed by refining the approximate numerosity estimator of the
system.

negative:
The Visual Dialogue task requires an agent to engage in a conversation about
an image with a human. It represents an extension of the Visual Question
Answering task in that the agent needs to answer a question about an image, but
it needs to do so in light of the previous dialogue that has taken place. The
key challenge in Visual Dialogue is thus maintaining a consistent, and natural
dialogue while continuing to answer questions correctly. We present a novel
approach that combines Reinforcement Learning and Generative Adversarial
Networks (GANs) to generate more human-like responses to questions. The GAN
helps overcome the relative paucity of training data, and the tendency of the
typical MLE-based approach to generate overly terse answers. Critically, the
GAN is tightly integrated into the attention mechanism that generates
human-interpretable reasons for each answer. This means that the discriminative
model of the GAN has the task of assessing whether a candidate answer is
generated by a human or not, given the provided reason. This is significant
because it drives the generative model to produce high quality answers that are
well supported by the associated reasoning. The method also generates the
state-of-the-art results on the primary benchmark.

negative:
The role of conversational assistants has become more prevalent in helping
people increase their productivity. Document-centered assistance, for example
to help an individual quickly review a document, has seen less significant
progress, even though it has the potential to tremendously increase a user's
productivity. This type of document-centered assistance is the focus of this
paper. Our contributions are three-fold: (1) We first present a survey to
understand the space of document-centered assistance and the capabilities
people expect in this scenario. (2) We investigate the types of queries that
users will pose while seeking assistance with documents, and show that
document-centered questions form the majority of these queries. (3) We present
a set of initial machine learned models that show that (a) we can accurately
detect document-centered questions, and (b) we can build reasonably accurate
models for answering such questions. These positive results are encouraging,
and suggest that even greater results may be attained with continued study of
this interesting and novel problem space. Our findings have implications for
the design of intelligent systems to support task completion via natural
interactions with documents.

negative:
In multiple-choice exams, students select one answer from among typically
four choices and can explain why they made that particular choice. Students are
good at understanding natural language questions and based on their domain
knowledge can easily infer the question's answer by 'connecting the dots'
across various pertinent facts.
  Considering automated reasoning for elementary science question answering, we
address the novel task of generating explanations for answers from
human-authored facts. For this, we examine the practically scalable framework
of feature-rich support vector machines leveraging domain-targeted,
hand-crafted features. Explanations are created from a human-annotated set of
nearly 5,000 candidate facts in the WorldTree corpus. Our aim is to obtain
better matches for valid facts of an explanation for the correct answer of a
question over the available fact candidates. To this end, our features offer a
comprehensive linguistic and semantic unification paradigm. The machine
learning problem is the preference ordering of facts, for which we test
pointwise regression versus pairwise learning-to-rank.
  Our contributions are: (1) a case study in which two preference ordering
approaches are systematically compared; (2) it is a practically competent
approach that can outperform some variants of BERT-based reranking models; and
(3) the human-engineered features make it an interpretable machine learning
model for the task.

negative:
Building a machine learning driven spoken dialog system for goal-oriented
interactions involves careful design of intents and data collection along with
development of intent recognition models and dialog policy learning algorithms.
The models should be robust enough to handle various user distractions during
the interaction flow and should steer the user back into an engaging
interaction for successful completion of the interaction. In this work, we have
designed a goal-oriented interaction system where children can engage with
agents for a series of interactions involving `Meet \& Greet' and `Simon Says'
game play. We have explored various feature extractors and models for improved
intent recognition and looked at leveraging previous user and system
interactions in novel ways with attention models. We have also looked at dialog
adaptation methods for entrained response selection. Our bootstrapped models
from limited training data perform better than many baseline approaches we have
looked at for intent recognition and dialog action prediction.

negative:
Neural network-based systems can now learn to locate the referents of words
and phrases in images, answer questions about visual scenes, and execute
symbolic instructions as first-person actors in partially-observable worlds. To
achieve this so-called grounded language learning, models must overcome
challenges that infants face when learning their first words. While it is
notable that models with no meaningful prior knowledge overcome these
obstacles, researchers currently lack a clear understanding of how they do so,
a problem that we attempt to address in this paper. For maximum control and
generality, we focus on a simple neural network-based language learning agent,
trained via policy-gradient methods, which can interpret single-word
instructions in a simulated 3D world. Whilst the goal is not to explicitly
model infant word learning, we take inspiration from experimental paradigms in
developmental psychology and apply some of these to the artificial agent,
exploring the conditions under which established human biases and learning
effects emerge. We further propose a novel method for visualising semantic
representations in the agent.

negative:
Context-aware neural machine translation (NMT) is a promising direction to
improve the translation quality by making use of the additional context, e.g.,
document-level translation, or having meta-information. Although there exist
various architectures and analyses, the effectiveness of different
context-aware NMT models is not well explored yet. This paper analyzes the
performance of document-level NMT models on four diverse domains with a varied
amount of parallel document-level bilingual data. We conduct a comprehensive
set of experiments to investigate the impact of document-level NMT. We find
that there is no single best approach to document-level NMT, but rather that
different architectures come out on top on different tasks. Looking at
task-specific problems, such as pronoun resolution or headline translation, we
find improvements in the context-aware systems, even in cases where the
corpus-level metrics like BLEU show no significant improvement. We also show
that document-level back-translation significantly helps to compensate for the
lack of document-level bi-texts.

negative:
The increasing incorporation of Artificial Intelligence in the form of
automated systems into decision-making procedures highlights not only the
importance of decision theory for automated systems but also the need for these
decision procedures to be explainable to the people involved in them.
Traditional realist accounts of explanation, wherein explanation is a relation
that holds (or does not hold) eternally between an explanans and an
explanandum, are not adequate to account for the notion of explanation required
for artificial decision procedures. We offer an alternative account of
explanation as used in the context of automated decision-making that makes
explanation an epistemic phenomenon, and one that is dependent on context. This
account of explanation better accounts for the way that we talk about, and use,
explanations and derived concepts, such as `explanatory power', and also allows
us to differentiate between reasons or causes on the one hand, which do not
need to have an epistemic aspect, and explanations on the other, which do have
such an aspect. Against this theoretical backdrop we then review existing
approaches to explanation in Artificial Intelligence and Machine Learning, and
suggest desiderata which truly explainable decision systems should fulfill.

negative:
Transportation services play a crucial part in the development of modern
smart cities. In particular, on-demand ridesharing services, which group
together passengers with similar itineraries, are already operating in several
metropolitan areas. These services can be of significant social and
environmental benefit, by reducing travel costs, road congestion and CO2
emissions.
  Unfortunately, despite their advantages, not many people opt to use these
ridesharing services. We believe that increasing the user satisfaction from the
service will cause more people to utilize it, which, in turn, will improve the
quality of the service, such as the waiting time, cost, travel time, and
service availability. One possible way for increasing user satisfaction is by
providing appropriate explanations comparing the alternative modes of
transportation, such as a private taxi ride and public transportation. For
example, a passenger may be more satisfied from a shared-ride if she is told
that a private taxi ride would have cost her 50% more. Therefore, the problem
is to develop an agent that provides explanations that will increase the user
satisfaction.
  We model our environment as a signaling game and show that a rational agent,
which follows the perfect Bayesian equilibrium, must reveal all of the
information regarding the possible alternatives to the passenger. In addition,
we develop a machine learning based agent that, when given a shared-ride along
with its possible alternatives, selects the explanations that are most likely
to increase user satisfaction. Using feedback from humans we show that our
machine learning based agent outperforms the rational agent and an agent that
randomly chooses explanations, in terms of user satisfaction.

negative:
Explainable machine learning attracts increasing attention as it improves
transparency of models, which is helpful for machine learning to be trusted in
real applications. However, explanation methods have recently been demonstrated
to be vulnerable to manipulation, where we can easily change a model's
explanation while keeping its prediction constant. To tackle this problem, some
efforts have been paid to use more stable explanation methods or to change
model configurations. In this work, we tackle the problem from the training
perspective, and propose a new training scheme called Adversarial Training on
EXplanations (ATEX) to improve the internal explanation stability of a model
regardless of the specific explanation method being applied. Instead of
directly specifying explanation values over data instances, ATEX only puts
requirement on model predictions which avoids involving second-order
derivatives in optimization. As a further discussion, we also find that
explanation stability is closely related to another property of the model,
i.e., the risk of being exposed to adversarial attack. Through experiments,
besides showing that ATEX improves model robustness against manipulation
targeting explanation, it also brings additional benefits including smoothing
explanations and improving the efficacy of adversarial training if applied to
the model.

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:
Future coalition operations can be substantially augmented through agile
teaming between human and machine agents, but in a coalition context these
agents may be unfamiliar to the human users and expected to operate in a broad
set of scenarios rather than being narrowly defined for particular purposes. In
such a setting it is essential that the human agents can rapidly build trust in
the machine agents through appropriate transparency of their behaviour, e.g.,
through explanations. The human agents are also able to bring their local
knowledge to the team, observing the situation unfolding and deciding which key
information should be communicated to the machine agents to enable them to
better account for the particular environment. In this paper we describe the
initial steps towards this human-agent knowledge fusion (HAKF) environment
through a recap of the key requirements, and an explanation of how these can be
fulfilled for an example situation. We show how HAKF has the potential to bring
value to both human and machine agents working as part of a distributed
coalition team in a complex event processing setting with uncertain sources.

negative:
Issues regarding explainable AI involve four components: users, laws &
regulations, explanations and algorithms. Together these components provide a
context in which explanation methods can be evaluated regarding their adequacy.
The goal of this chapter is to bridge the gap between expert users and lay
users. Different kinds of users are identified and their concerns revealed,
relevant statements from the General Data Protection Regulation are analyzed in
the context of Deep Neural Networks (DNNs), a taxonomy for the classification
of existing explanation methods is introduced, and finally, the various classes
of explanation methods are analyzed to verify if user concerns are justified.
Overall, it is clear that (visual) explanations can be given about various
aspects of the influence of the input on the output. However, it is noted that
explanation methods or interfaces for lay users are missing and we speculate
which criteria these methods / interfaces should satisfy. Finally it is noted
that two important concerns are difficult to address with explanation methods:
the concern about bias in datasets that leads to biased DNNs, as well as the
suspicion about unfair outcomes.

negative:
The complexity of state-of-the-art modeling techniques for image
classification impedes the ability to explain model predictions in an
interpretable way. Existing explanation methods generally create importance
rankings in terms of pixels or pixel groups. However, the resulting
explanations lack an optimal size, do not consider feature dependence and are
only related to one class. Counterfactual explanation methods are considered
promising to explain complex model decisions, since they are associated with a
high degree of human interpretability. In this paper, SEDC is introduced as a
model-agnostic instance-level explanation method for image classification to
obtain visual counterfactual explanations. For a given image, SEDC searches a
small set of segments that, in case of removal, alters the classification. As
image classification tasks are typically multiclass problems, SEDC-T is
proposed as an alternative method that allows specifying a target
counterfactual class. We compare SEDC(-T) with popular feature importance
methods such as LRP, LIME and SHAP, and we describe how the mentioned
importance ranking issues are addressed. Moreover, concrete examples and
experiments illustrate the potential of our approach (1) to obtain trust and
insight, and (2) to obtain input for model improvement by explaining
misclassifications.

negative:
In this document I present an approach to answer validation and reranking for
question answering (QA) systems. A cased-based reasoning (CBR) system judges
answer candidates for questions from annotated answer candidates for earlier
questions. The promise of this approach is that user feedback will result in
improved answers of the QA system, due to the growing case base. In the paper,
I present the adequate structuring of the case base and the appropriate
selection of relevant similarity measures, in order to solve the answer
validation problem. The structural case base is built from annotated MultiNet
graphs, which provide representations for natural language expressions, and
corresponding graph similarity measures. I cover a priori relations to
experienced answer candidates for former questions. I compare the CBR System
results to current approaches in an experiment integrating CBR into an existing
framework for answer validation and reranking. This integration is achieved by
adding CBR-related features to the input of a learned ranking model that
determines the final answer ranking. In the experiments based on QA@CLEF
questions, the best learned models make heavy use of CBR features. Observing
the results with a continually growing case base, I present a positive effect
of the size of the case base on the accuracy of the CBR subsystem.

negative:
With the ever-increasing use of complex machine learning models in critical
applications within the finance domain, explaining the decisions of the model
has become a necessity. With applications spanning from credit scoring to
credit marketing, the impact of these models is undeniable. Among the multiple
ways in which one can explain the decisions of these complicated models, local
post hoc model agnostic explanations have gained massive adoption. These
methods allow one to explain each prediction independent of the modelling
technique that was used while training. As explanations, they either give
individual feature attributions or provide sufficient rules that represent
conditions for a prediction to be made. The current state of the art methods
use rudimentary methods to generate synthetic data around the point to be
explained. This is followed by fitting simple linear models as surrogates to
obtain a local interpretation of the prediction. In this paper, we seek to
significantly improve on both, the method used to generate the explanations and
the nature of explanations produced. We use a Generative Adversarial Network
for synthetic data generation and train a piecewise linear model in the form of
Linear Model Trees to be used as the surrogate model.In addition to individual
feature attributions, we also provide an accompanying context to our
explanations by leveraging the structure and property of our surrogate model.

negative:
Generating explanation to explain its behavior is an essential capability for
a robotic teammate. Explanations help human partners better understand the
situation and maintain trust of their teammates. Prior work on robot generating
explanations focuses on providing the reasoning behind its decision making.
These approaches, however, fail to heed the cognitive requirement of
understanding an explanation. In other words, while they provide the right
explanations from the explainer's perspective, the explainee part of the
equation is ignored. In this work, we address an important aspect along this
direction that contributes to a better understanding of a given explanation,
which we refer to as the progressiveness of explanations. A progressive
explanation improves understanding by limiting the cognitive effort required at
each step of making the explanation. As a result, such explanations are
expected to be smoother and hence easier to understand. A general formulation
of progressive explanation is presented. Algorithms are provided based on
several alternative quantifications of cognitive effort as an explanation is
being made, which are evaluated in a standard planning competition domain.

negative:
Problems at the intersection of vision and language are of significant
importance both as challenging research questions and for the rich set of
applications they enable. However, inherent structure in our world and bias in
our language tend to be a simpler signal for learning than visual modalities,
resulting in models that ignore visual information, leading to an inflated
sense of their capability.
  We propose to counter these language priors for the task of Visual Question
Answering (VQA) and make vision (the V in VQA) matter! Specifically, we balance
the popular VQA dataset by collecting complementary images such that every
question in our balanced dataset is associated with not just a single image,
but rather a pair of similar images that result in two different answers to the
question. Our dataset is by construction more balanced than the original VQA
dataset and has approximately twice the number of image-question pairs. Our
complete balanced dataset is publicly available at www.visualqa.org as part of
the 2nd iteration of the Visual Question Answering Dataset and Challenge (VQA
v2.0).
  We further benchmark a number of state-of-art VQA models on our balanced
dataset. All models perform significantly worse on our balanced dataset,
suggesting that these models have indeed learned to exploit language priors.
This finding provides the first concrete empirical evidence for what seems to
be a qualitative sense among practitioners.
  Finally, our data collection protocol for identifying complementary images
enables us to develop a novel interpretable model, which in addition to
providing an answer to the given (image, question) pair, also provides a
counter-example based explanation. Specifically, it identifies an image that is
similar to the original image, but it believes has a different answer to the
same question. This can help in building trust for machines among their users.

negative:
Recent years have seen the introduction of a range of methods for post-hoc
explainability of image classifier predictions. However, these post-hoc
explanations may not always be faithful to classifier predictions, which poses
a significant challenge when attempting to debug models based on such
explanations. To this end, we seek a methodology that can improve the
faithfulness of an explanation method with respect to model predictions which
does not require ground truth explanations. We achieve this through a novel
explanation-driven data augmentation (EDDA) technique that augments the
training data with occlusions inferred from model explanations; this is based
on the simple motivating principle that \emph{if} the explainer is faithful to
the model \emph{then} occluding salient regions for the model prediction should
decrease the model confidence in the prediction, while occluding non-salient
regions should not change the prediction. To verify that the proposed
augmentation method has the potential to improve faithfulness, we evaluate EDDA
using a variety of datasets and classification models. We demonstrate
empirically that our approach leads to a significant increase of faithfulness,
which can facilitate better debugging and successful deployment of image
classification models in real-world applications.

negative:
Our research is a step toward ascertaining the need for personalization, in
XAI, and we do so in the context of investigating the value of explanations of
AI-driven hints and feedback are useful in Intelligent Tutoring Systems (ITS).
We added an explanation functionality for the adaptive hints provided by the
Adaptive CSP (ACSP) applet, an interactive simulation that helps students learn
an algorithm for constraint satisfaction problems by providing AI-driven hints
adapted to their predicted level of learning. We present the design of the
explanation functionality and the results of a controlled study to evaluate its
impact on students' learning and perception of the ACPS hints. The study
includes an analysis of how these outcomes are modulated by several user
characteristics such as personality traits and cognitive abilities, to asses if
explanations should be personalized to these characteristics. Our results
indicate that providing explanations increase students' trust in the ACPS
hints, perceived usefulness of the hints, and intention to use them again. In
addition, we show that students' access of the explanation and learning gains
are modulated by user characteristics, providing insights toward designing
personalized Explainable AI (XAI) for ITS.

negative:
In the last years many accurate decision support systems have been
constructed as black boxes, that is as systems that hide their internal logic
to the user. This lack of explanation constitutes both a practical and an
ethical issue. The literature reports many approaches aimed at overcoming this
crucial weakness sometimes at the cost of scarifying accuracy for
interpretability. The applications in which black box decision systems can be
used are various, and each approach is typically developed to provide a
solution for a specific problem and, as a consequence, delineating explicitly
or implicitly its own definition of interpretability and explanation. The aim
of this paper is to provide a classification of the main problems addressed in
the literature with respect to the notion of explanation and the type of black
box system. Given a problem definition, a black box type, and a desired
explanation this survey should help the researcher to find the proposals more
useful for his own work. The proposed classification of approaches to open
black box models should also be useful for putting the many research open
questions in perspective.

negative:
We present a randomized controlled trial for a model-in-the-loop regression
task, with the goal of measuring the extent to which (1) good explanations of
model predictions increase human accuracy, and (2) faulty explanations decrease
human trust in the model. We study explanations based on visual saliency in an
image-based age prediction task for which humans and learned models are
individually capable but not highly proficient and frequently disagree. Our
experimental design separates model quality from explanation quality, and makes
it possible to compare treatments involving a variety of explanations of
varying levels of quality. We find that presenting model predictions improves
human accuracy. However, visual explanations of various kinds fail to
significantly alter human accuracy or trust in the model - regardless of
whether explanations characterize an accurate model, an inaccurate one, or are
generated randomly and independently of the input image. These findings suggest
the need for greater evaluation of explanations in downstream decision making
tasks, better design-based tools for presenting explanations to users, and
better approaches for generating explanations.

negative:
In automated planning, the need for explanations arises when there is a
mismatch between a proposed plan and the user's expectation. We frame
Explainable AI Planning in the context of the plan negotiation problem, in
which a succession of hypothetical planning problems are generated and solved.
The object of the negotiation is for the user to understand and ultimately
arrive at a satisfactory plan. We present the results of a user study that
demonstrates that when users ask questions about plans, those questions are
contrastive, i.e. "why A rather than B?". We use the data from this study to
construct a taxonomy of user questions that often arise during plan
negotiation. We formally define our approach to plan negotiation through model
restriction as an iterative process. This approach generates hypothetical
problems and contrastive plans by restricting the model through constraints
implied by user questions. We formally define model-based compilations in
PDDL2.1 of each constraint derived from a user question in the taxonomy, and
empirically evaluate the compilations in terms of computational complexity. The
compilations were implemented as part of an explanation framework that employs
iterative model restriction. We demonstrate its benefits in a second user
study.

negative:
A feature-based model explanation denotes how much each input feature
contributes to a model's output for a given data point. As the number of
proposed explanation functions grows, we lack quantitative evaluation criteria
to help practitioners know when to use which explanation function. This paper
proposes quantitative evaluation criteria for feature-based explanations: low
sensitivity, high faithfulness, and low complexity. We devise a framework for
aggregating explanation functions. We develop a procedure for learning an
aggregate explanation function with lower complexity and then derive a new
aggregate Shapley value explanation function that minimizes sensitivity.

negative:
Theoretically as well as experimentally it is investigated how people
represent their knowledge in order to make decisions or to share their
knowledge with others. Experiment 1 probes into the ways how people 6ather
information about the frequencies of events and how the requested response
mode, that is, numerical vs. verbal estimates interferes with this knowledge.
The least interference occurs if the subjects are allowed to give verbal
responses. From this it is concluded that processing knowledge about
uncertainty categorically, that is, by means of verbal expressions, imposes
less mental work load on the decision matter than numerical processing.
Possibility theory is used as a framework for modeling the individual usage of
verbal categories for grades of uncertainty. The 'elastic' constraints on the
verbal expressions for every sing1e subject are determined in Experiment 2 by
means of sequential calibration. In further experiments it is shown that the
superiority of the verbal processing of knowledge about uncertainty guise
generally reduces persistent biases reported in the literature: conservatism
(Experiment 3) and neg1igence of regression (Experiment 4). The reanalysis of
Hormann's data reveal that in verbal Judgments people exhibit sensitivity for
base rates and are not prone to the conjunction fallacy. In a final experiment
(5) about predictions in a real-life situation it turns out that in a numerical
forecasting task subjects restricted themselves to those parts of their
knowledge which are numerical. On the other hand subjects in a verbal
forecasting task accessed verbally as well as numerically stated knowledge.
Forecasting is structurally related to the estimation of probabilities for rare
events insofar as supporting and contradicting arguments have to be evaluated
and the choice of the final Judgment has to be Justified according to the
evidence brought forward. In order to assist people in such choice situations a
formal model for the interactive checking of arguments has been developed. The
model transforms the normal-language quantifiers used in the arguments into
fuzzy numbers and evaluates the given train of arguments by means of fuzzy
numerica1 operations. Ambiguities in the meanings of quantifiers are resolved
interactively.

negative:
To increase trust in artificial intelligence systems, a promising research
direction consists of designing neural models capable of generating natural
language explanations for their predictions. In this work, we show that such
models are nonetheless prone to generating mutually inconsistent explanations,
such as "Because there is a dog in the image" and "Because there is no dog in
the [same] image", exposing flaws in either the decision-making process of the
model or in the generation of the explanations. We introduce a simple yet
effective adversarial framework for sanity checking models against the
generation of inconsistent natural language explanations. Moreover, as part of
the framework, we address the problem of adversarial attacks with full target
sequences, a scenario that was not previously addressed in sequence-to-sequence
attacks. Finally, we apply our framework on a state-of-the-art neural natural
language inference model that provides natural language explanations for its
predictions. Our framework shows that this model is capable of generating a
significant number of inconsistent explanations.

negative:
The main objective of eXplainable Artificial Intelligence (XAI) is to provide
effective explanations for black-box classifiers. The existing literature lists
many desirable properties for explanations to be useful, but there is no
consensus on how to quantitatively evaluate explanations in practice. Moreover,
explanations are typically used only to inspect black-box models, and the
proactive use of explanations as a decision support is generally overlooked.
Among the many approaches to XAI, a widely adopted paradigm is Local Linear
Explanations - with LIME and SHAP emerging as state-of-the-art methods. We show
that these methods are plagued by many defects including unstable explanations,
divergence of actual implementations from the promised theoretical properties,
and explanations for the wrong label. This highlights the need to have standard
and unbiased evaluation procedures for Local Linear Explanations in the XAI
field. In this paper we address the problem of identifying a clear and
unambiguous set of metrics for the evaluation of Local Linear Explanations.
This set includes both existing and novel metrics defined specifically for this
class of explanations. All metrics have been included in an open Python
framework, named LEAF. The purpose of LEAF is to provide a reference for end
users to evaluate explanations in a standardised and unbiased way, and to guide
researchers towards developing improved explainable techniques.

negative:
Autonomous agents situated in real-world environments must be able to master
large repertoires of skills. While a single short skill can be learned quickly,
it would be impractical to learn every task independently. Instead, the agent
should share knowledge across behaviors such that each task can be learned
efficiently, and such that the resulting model can generalize to new tasks,
especially ones that are compositions or subsets of tasks seen previously. A
policy conditioned on a goal or demonstration has the potential to share
knowledge between tasks if it sees enough diversity of inputs. However, these
methods may not generalize to a more complex task at test time. We introduce
compositional plan vectors (CPVs) to enable a policy to perform compositions of
tasks without additional supervision. CPVs represent trajectories as the sum of
the subtasks within them. We show that CPVs can be learned within a one-shot
imitation learning framework without any additional supervision or information
about task hierarchy, and enable a demonstration-conditioned policy to
generalize to tasks that sequence twice as many skills as the tasks seen during
training.
  Analogously to embeddings such as word2vec in NLP, CPVs can also support
simple arithmetic operations -- for example, we can add the CPVs for two
different tasks to command an agent to compose both tasks, without any
additional training.

negative:
Local explanation methods such as LIME have become popular in MIR as tools
for generating post-hoc, model-agnostic explanations of a model's
classification decisions. The basic idea is to identify a small set of
human-understandable features of the classified example that are most
influential on the classifier's prediction. These are then presented as an
explanation. Evaluation of such explanations in publications often resorts to
accepting what matches the expectation of a human without actually being able
to verify if what the explanation shows is what really caused the model's
prediction. This paper reports on targeted investigations where we try to get
more insight into the actual veracity of LIME's explanations in an audio
classification task. We deliberately design adversarial examples for the
classifier, in a way that gives us knowledge about which parts of the input are
potentially responsible for the model's (wrong) prediction. Asking LIME to
explain the predictions for these adversaries permits us to study whether local
explanations do indeed detect these regions of interest. We also look at
whether LIME is more successful in finding perturbations that are more
prominent and easily noticeable for a human. Our results suggest that LIME does
not necessarily manage to identify the most relevant input features and hence
it remains unclear whether explanations are useful or even misleading.

negative:
The widespread adoption of black-box models in Artificial Intelligence has
enhanced the need for explanation methods to reveal how these obscure models
reach specific decisions. Retrieving explanations is fundamental to unveil
possible biases and to resolve practical or ethical issues. Nowadays, the
literature is full of methods with different explanations. We provide a
categorization of explanation methods based on the type of explanation
returned. We present the most recent and widely used explainers, and we show a
visual comparison among explanations and a quantitative benchmarking.

negative:
eXplainable AI focuses on generating explanations for the output of an AI
algorithm to a user, usually a decision-maker. Such user needs to interpret the
AI system in order to decide whether to trust the machine outcome. When
addressing this challenge, therefore, proper attention should be given to
produce explanations that are interpretable by the target community of users.
In this chapter, we claim for the need to better investigate what constitutes a
human explanation, i.e. a justification of the machine behaviour that is
interpretable and actionable by the human decision makers. In particular, we
focus on the contributions that Human Intelligence can bring to eXplainable AI,
especially in conjunction with the exploitation of Knowledge Graphs. Indeed, we
call for a better interplay between Knowledge Representation and Reasoning,
Social Sciences, Human Computation and Human-Machine Cooperation research -- as
already explored in other AI branches -- in order to support the goal of
eXplainable AI with the adoption of a Human-in-the-Loop approach.

negative:
Owing to the rapidly growing multimedia content available on the Internet,
extractive spoken document summarization, with the purpose of automatically
selecting a set of representative sentences from a spoken document to concisely
express the most important theme of the document, has been an active area of
research and experimentation. On the other hand, word embedding has emerged as
a newly favorite research subject because of its excellent performance in many
natural language processing (NLP)-related tasks. However, as far as we are
aware, there are relatively few studies investigating its use in extractive
text or speech summarization. A common thread of leveraging word embeddings in
the summarization process is to represent the document (or sentence) by
averaging the word embeddings of the words occurring in the document (or
sentence). Then, intuitively, the cosine similarity measure can be employed to
determine the relevance degree between a pair of representations. Beyond the
continued efforts made to improve the representation of words, this paper
focuses on building novel and efficient ranking models based on the general
word embedding methods for extractive speech summarization. Experimental
results demonstrate the effectiveness of our proposed methods, compared to
existing state-of-the-art methods.

negative:
We administered the Verbal IQ (VIQ) part of the Wechsler Preschool and
Primary Scale of Intelligence (WPPSI-III) to the ConceptNet 4 AI system. The
test questions (e.g., "Why do we shake hands?") were translated into ConceptNet
4 inputs using a combination of the simple natural language processing tools
that come with ConceptNet together with short Python programs that we wrote.
The question answering used a version of ConceptNet based on spectral methods.
The ConceptNet system scored a WPPSI-III VIQ that is average for a
four-year-old child, but below average for 5 to 7 year-olds. Large variations
among subtests indicate potential areas of improvement. In particular, results
were strongest for the Vocabulary and Similarities subtests, intermediate for
the Information subtest, and lowest for the Comprehension and Word Reasoning
subtests. Comprehension is the subtest most strongly associated with common
sense. The large variations among subtests and ordinary common sense strongly
suggest that the WPPSI-III VIQ results do not show that "ConceptNet has the
verbal abilities a four-year-old." Rather, children's IQ tests offer one
objective metric for the evaluation and comparison of AI systems. Also, this
work continues previous research on Psychometric AI.

negative:
After learning a concept, humans are also able to continually generalize
their learned concepts to new domains by observing only a few labeled instances
without any interference with the past learned knowledge. In contrast, learning
concepts efficiently in a continual learning setting remains an open challenge
for current Artificial Intelligence algorithms as persistent model retraining
is necessary. Inspired by the Parallel Distributed Processing learning and the
Complementary Learning Systems theories, we develop a computational model that
is able to expand its previously learned concepts efficiently to new domains
using a few labeled samples. We couple the new form of a concept to its past
learned forms in an embedding space for effective continual learning. Doing so,
a generative distribution is learned such that it is shared across the tasks in
the embedding space and models the abstract concepts. This procedure enables
the model to generate pseudo-data points to replay the past experience to
tackle catastrophic forgetting.

negative:
Given questions regarding some prototypical situation such as Name something
that people usually do before they leave the house for work? a human can easily
answer them via acquired experiences. There can be multiple right answers for
such questions, with some more common for a situation than others. This paper
introduces a new question answering dataset for training and evaluating common
sense reasoning capabilities of artificial intelligence systems in such
prototypical situations. The training set is gathered from an existing set of
questions played in a long-running international game show FAMILY- FEUD. The
hidden evaluation set is created by gathering answers for each question from
100 crowd-workers. We also propose a generative evaluation task where a model
has to output a ranked list of answers, ideally covering all prototypical
answers for a question. After presenting multiple competitive baseline models,
we find that human performance still exceeds model scores on all evaluation
metrics with a meaningful gap, supporting the challenging nature of the task.

negative:
Common sense has always been of interest in Artificial Intelligence, but has
rarely taken center stage. Despite its mention in one of John McCarthy's
earliest papers and years of work by dedicated researchers, arguably no AI
system with a serious amount of general common sense has ever emerged. Why is
that? What's missing? Examples of AI systems' failures of common sense abound,
and they point to AI's frequent focus on expertise as the cause. Those
attempting to break the resulting brittleness barrier, even in the context of
modern deep learning, have tended to invest their energy in large numbers of
small bits of commonsense knowledge. While important, all the commonsense
knowledge fragments in the world don't add up to a system that actually
demonstrates common sense in a human-like way. We advocate examining common
sense from a broader perspective than in the past. Common sense should be
considered in the context of a full cognitive system with history, goals,
desires, and drives, not just in isolated circumscribed examples. A fresh look
is needed: common sense is worthy of its own dedicated scientific exploration.

negative:
Cross-lingual document representations enable language understanding in
multilingual contexts and allow transfer learning from high-resource to
low-resource languages at the document level. Recently large pre-trained
language models such as BERT, XLM and XLM-RoBERTa have achieved great success
when fine-tuned on sentence-level downstream tasks. It is tempting to apply
these cross-lingual models to document representation learning. However, there
are two challenges: (1) these models impose high costs on long document
processing and thus many of them have strict length limit; (2) model
fine-tuning requires extra data and computational resources, which is not
practical in resource-limited settings. In this work, we address these
challenges by proposing unsupervised Language-Agnostic Weighted Document
Representations (LAWDR). We study the geometry of pre-trained sentence
embeddings and leverage it to derive document representations without
fine-tuning. Evaluated on cross-lingual document alignment, LAWDR demonstrates
comparable performance to state-of-the-art models on benchmark datasets.

negative:
This paper describes an application of reinforcement learning to the mention
detection task. We define a novel action-based formulation for the mention
detection task, in which a model can flexibly revise past labeling decisions by
grouping together tokens and assigning partial mention labels. We devise a
method to create mention-level episodes and we train a model by rewarding
correctly labeled complete mentions, irrespective of the inner structure
created. The model yields results which are on par with a competitive
supervised counterpart while being more flexible in terms of achieving targeted
behavior through reward modeling and generating internal mention structure,
especially on longer mentions.

negative:
Neural networks with deep architectures have demonstrated significant
performance improvements in computer vision, speech recognition, and natural
language processing. The challenges in information retrieval (IR), however, are
different from these other application areas. A common form of IR involves
ranking of documents--or short passages--in response to keyword-based queries.
Effective IR systems must deal with query-document vocabulary mismatch problem,
by modeling relationships between different query and document terms and how
they indicate relevance. Models should also consider lexical matches when the
query contains rare terms--such as a person's name or a product model
number--not seen during training, and to avoid retrieving semantically related
but irrelevant results. In many real-life IR tasks, the retrieval involves
extremely large collections--such as the document index of a commercial Web
search engine--containing billions of documents. Efficient IR methods should
take advantage of specialized IR data structures, such as inverted index, to
efficiently retrieve from large collections. Given an information need, the IR
system also mediates how much exposure an information artifact receives by
deciding whether it should be displayed, and where it should be positioned,
among other results. Exposure-aware IR systems may optimize for additional
objectives, besides relevance, such as parity of exposure for retrieved items
and content publishers. In this thesis, we present novel neural architectures
and methods motivated by the specific needs and challenges of IR tasks.

negative:
There has been much discussion of the right to explanation in the EU General
Data Protection Regulation, and its existence, merits, and disadvantages.
Implementing a right to explanation that opens the black box of algorithmic
decision-making faces major legal and technical barriers. Explaining the
functionality of complex algorithmic decision-making systems and their
rationale in specific cases is a technically challenging problem. Some
explanations may offer little meaningful information to data subjects, raising
questions around their value. Explanations of automated decisions need not
hinge on the general public understanding how algorithmic systems function.
Even though such interpretability is of great importance and should be pursued,
explanations can, in principle, be offered without opening the black box.
Looking at explanations as a means to help a data subject act rather than
merely understand, one could gauge the scope and content of explanations
according to the specific goal or action they are intended to support. From the
perspective of individuals affected by automated decision-making, we propose
three aims for explanations: (1) to inform and help the individual understand
why a particular decision was reached, (2) to provide grounds to contest the
decision if the outcome is undesired, and (3) to understand what would need to
change in order to receive a desired result in the future, based on the current
decision-making model. We assess how each of these goals finds support in the
GDPR. We suggest data controllers should offer a particular type of
explanation, unconditional counterfactual explanations, to support these three
aims. These counterfactual explanations describe the smallest change to the
world that can be made to obtain a desirable outcome, or to arrive at the
closest possible world, without needing to explain the internal logic of the
system.

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:
Recently, many AI researchers and practitioners have embarked on research
visions that involve doing AI for "Good". This is part of a general drive
towards infusing AI research and practice with ethical thinking. One frequent
theme in current ethical guidelines is the requirement that AI be good for all,
or: contribute to the Common Good. But what is the Common Good, and is it
enough to want to be good? Via four lead questions, I will illustrate
challenges and pitfalls when determining, from an AI point of view, what the
Common Good is and how it can be enhanced by AI. The questions are: What is the
problem / What is a problem?, Who defines the problem?, What is the role of
knowledge?, and What are important side effects and dynamics? The illustration
will use an example from the domain of "AI for Social Good", more specifically
"Data Science for Social Good". Even if the importance of these questions may
be known at an abstract level, they do not get asked sufficiently in practice,
as shown by an exploratory study of 99 contributions to recent conferences in
the field. Turning these challenges and pitfalls into a positive
recommendation, as a conclusion I will draw on another characteristic of
computer-science thinking and practice to make these impediments visible and
attenuate them: "attacks" as a method for improving design. This results in the
proposal of ethics pen-testing as a method for helping AI designs to better
contribute to the Common Good.

negative:
Document clustering is a text mining technique used to provide better
document search and browsing in digital libraries or online corpora. A lot of
research has been done on biomedical document clustering that is based on using
existing ontology. But, associations and co-occurrences of the medical concepts
are not well represented by using ontology. In this research, a vector
representation of concepts of diseases and similarity measurement between
concepts are proposed. They identify the closest concepts of diseases in the
context of a corpus. Each document is represented by using the vector space
model. A weight scheme is proposed to consider both local content and
associations between concepts. A Self-Organizing Map is used as document
clustering algorithm. The vector projection and visualization features of SOM
enable visualization and analysis of the clusters distributions and
relationships on the two dimensional space. The experimental results show that
the proposed document clustering framework generates meaningful clusters and
facilitate visualization of the clusters based on the concepts of diseases.

negative:
We evaluate four computational models of explanation in Bayesian networks by
comparing model predictions to human judgments. In two experiments, we present
human participants with causal structures for which the models make divergent
predictions and either solicit the best explanation for an observed event
(Experiment 1) or have participants rate provided explanations for an observed
event (Experiment 2). Across two versions of two causal structures and across
both experiments, we find that the Causal Explanation Tree and Most Relevant
Explanation models provide better fits to human data than either Most Probable
Explanation or Explanation Tree models. We identify strengths and shortcomings
of these models and what they can reveal about human explanation. We conclude
by suggesting the value of pursuing computational and psychological
investigations of explanation in parallel.

negative:
Relation extraction (RE) aims to identify the semantic relations between
named entities in text. Recent years have witnessed it raised to the document
level, which requires complex reasoning with entities and mentions throughout
an entire document. In this paper, we propose a novel model to document-level
RE, by encoding the document information in terms of entity global and local
representations as well as context relation representations. Entity global
representations model the semantic information of all entities in the document,
entity local representations aggregate the contextual information of multiple
mentions of specific entities, and context relation representations encode the
topic information of other relations. Experimental results demonstrate that our
model achieves superior performance on two public datasets for document-level
RE. It is particularly effective in extracting relations between entities of
long distance and having multiple mentions.

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:
Children learning their first language face multiple problems of induction:
how to learn the meanings of words, and how to build meaningful phrases from
those words according to syntactic rules. We consider how children might solve
these problems efficiently by solving them jointly, via a computational model
that learns the syntax and semantics of multi-word utterances in a grounded
reference game. We select a well-studied empirical case in which children are
aware of patterns linking the syntactic and semantic properties of words ---
that the properties picked out by base nouns tend to be related to shape, while
prenominal adjectives tend to refer to other properties such as color. We show
that children applying such inductive biases are accurately reflecting the
statistics of child-directed speech, and that inducing similar biases in our
computational model captures children's behavior in a classic adjective
learning experiment. Our model incorporating such biases also demonstrates a
clear data efficiency in learning, relative to a baseline model that learns
without forming syntax-sensitive overhypotheses of word meaning. Thus solving a
more complex joint inference problem may make the full problem of language
acquisition easier, not harder.

negative:
Document-level relation extraction aims to discover relations between
entities across a whole document. How to build the dependency of entities from
different sentences in a document remains to be a great challenge. Current
approaches either leverage syntactic trees to construct document-level graphs
or aggregate inference information from different sentences. In this paper, we
build cross-sentence dependencies by inferring compositional relations between
inter-sentence mentions. Adopting aggressive linking strategy, intermediate
relations are reasoned on the document-level graphs by mention convolution. We
further notice the generalization problem of NA instances, which is caused by
incomplete annotation and worsened by fully-connected mention pairs. An
improved ranking loss is proposed to attend this problem. Experiments show the
connections between different mentions are crucial to document-level relation
extraction, which enables the model to extract more meaningful higher-level
compositional relations.

negative:
The concept of innateness is rarely discussed in the context of artificial
intelligence. When it is discussed, or hinted at, it is often the context of
trying to reduce the amount of innate machinery in a given system. In this
paper, I consider as a test case a recent series of papers by Silver et al
(Silver et al., 2017a) on AlphaGo and its successors that have been presented
as an argument that a "even in the most challenging of domains: it is possible
to train to superhuman level, without human examples or guidance", "starting
tabula rasa."
  I argue that these claims are overstated, for multiple reasons. I close by
arguing that artificial intelligence needs greater attention to innateness, and
I point to some proposals about what that innateness might look like.

negative:
Probability theory, epistemically interpreted, provides an excellent, if not
the best available account of inductive reasoning. This is so because there are
general and definite rules for the change of subjective probabilities through
information or experience; induction and belief change are one and same topic,
after all. The most basic of these rules is simply to conditionalize with
respect to the information received; and there are similar and more general
rules. 1 Hence, a fundamental reason for the epistemological success of
probability theory is that there at all exists a well-behaved concept of
conditional probability. Still, people have, and have reasons for, various
concerns over probability theory. One of these is my starting point:
Intuitively, we have the notion of plain belief; we believe propositions2 to be
true (or to be false or neither). Probability theory, however, offers no formal
counterpart to this notion. Believing A is not the same as having probability 1
for A, because probability 1 is incorrigible3; but plain belief is clearly
corrigible. And believing A is not the same as giving A a probability larger
than some 1 - c, because believing A and believing B is usually taken to be
equivalent to believing A & B.4 Thus, it seems that the formal representation
of plain belief has to take a non-probabilistic route. Indeed, representing
plain belief seems easy enough: simply represent an epistemic state by the set
of all propositions believed true in it or, since I make the common assumption
that plain belief is deductively closed, by the conjunction of all propositions
believed true in it. But this does not yet provide a theory of induction, i.e.
an answer to the question how epistemic states so represented are changed
tbrough information or experience. There is a convincing partial answer: if the
new information is compatible with the old epistemic state, then the new
epistemic state is simply represented by the conjunction of the new information
and the old beliefs. This answer is partial because it does not cover the quite
common case where the new information is incompatible with the old beliefs. It
is, however, important to complete the answer and to cover this case, too;
otherwise, we would not represent plain belief as conigible. The crucial
problem is that there is no good completion. When epistemic states are
represented simply by the conjunction of all propositions believed true in it,
the answer cannot be completed; and though there is a lot of fruitful work, no
other representation of epistemic states has been proposed, as far as I know,
which provides a complete solution to this problem. In this paper, I want to
suggest such a solution. In [4], I have more fully argued that this is the only
solution, if certain plausible desiderata are to be satisfied. Here, in section
2, I will be content with formally defining and intuitively explaining my
proposal. I will compare my proposal with probability theory in section 3. It
will turn out that the theory I am proposing is structurally homomorphic to
probability theory in important respects and that it is thus equally easily
implementable, but moreover computationally simpler. Section 4 contains a very
brief comparison with various kinds of logics, in particular conditional logic,
with Shackle's functions of potential surprise and related theories, and with
the Dempster - Shafer theory of belief functions.

negative:
The rise of artificial intelligence (A.I.) based systems is already offering
substantial benefits to the society as a whole. However, these systems may also
enclose potential conflicts and unintended consequences. Notably, people will
tend to adopt an A.I. system if it confers them an advantage, at which point
non-adopters might push for a strong regulation if that advantage for adopters
is at a cost for them. Here we propose an agent-based game-theoretical model
for these conflicts, where agents may decide to resort to A.I. to use and
acquire additional information on the payoffs of a stochastic game, striving to
bring insights from simulation to what has been, hitherto, a mostly
philosophical discussion. We frame our results under the current discussion on
ethical A.I. and the conflict between individual and societal gains: the
societal value alignment problem. We test the arising equilibria in the
adoption of A.I. technology under different norms followed by artificial
agents, their ensuing benefits, and the emergent levels of wealth inequality.
We show that without any regulation, purely selfish A.I. systems will have the
strongest advantage, even when a utilitarian A.I. provides significant benefits
for the individual and the society. Nevertheless, we show that it is possible
to develop A.I. systems following human conscious policies that, when
introduced in society, lead to an equilibrium where the gains for the adopters
are not at a cost for non-adopters, thus increasing the overall wealth of the
population and lowering inequality. However, as shown, a self-organised
adoption of such policies would require external regulation.

negative:
Question answering and conversational systems are often baffled and need help
clarifying certain ambiguities. However, limitations of existing datasets
hinder the development of large-scale models capable of generating and
utilising clarification questions. In order to overcome these limitations, we
devise a novel bootstrapping framework (based on self-supervision) that assists
in the creation of a diverse, large-scale dataset of clarification questions
based on post-comment tuples extracted from stackexchange. The framework
utilises a neural network based architecture for classifying clarification
questions. It is a two-step method where the first aims to increase the
precision of the classifier and second aims to increase its recall. We
quantitatively demonstrate the utility of the newly created dataset by applying
it to the downstream task of question-answering. The final dataset, ClarQ,
consists of ~2M examples distributed across 173 domains of stackexchange. We
release this dataset in order to foster research into the field of
clarification question generation with the larger goal of enhancing dialog and
question answering systems.

negative:
We propose a method for online news stream clustering that is a variant of
the non-parametric streaming K-means algorithm. Our model uses a combination of
sparse and dense document representations, aggregates document-cluster
similarity along these multiple representations and makes the clustering
decision using a neural classifier. The weighted document-cluster similarity
model is learned using a novel adaptation of the triplet loss into a linear
classification objective. We show that the use of a suitable fine-tuning
objective and external knowledge in pre-trained transformer models yields
significant improvements in the effectiveness of contextual embeddings for
clustering. Our model achieves a new state-of-the-art on a standard stream
clustering dataset of English documents.

negative:
Transferring as fast as possible the functioning of our brain to artificial
intelligence is an ambitious goal that would help advance the state of the art
in AI and robotics. It is in this perspective that we propose to start from
hypotheses derived from an empirical study in a human-robot interaction and to
verify if they are validated in the same way for children as for a basic
reinforcement learning algorithm. Thus, we check whether receiving help from an
expert when solving a simple close-ended task (the Towers of Hano\"i) allows to
accelerate or not the learning of this task, depending on whether the
intervention is canonical or requested by the player. Our experiences have
allowed us to conclude that, whether requested or not, a Q-learning algorithm
benefits in the same way from expert help as children do.

negative:
What is the nature of curiosity? Is there any scientific way to understand
the origin of this mysterious force that drives the behavior of even the
stupidest naturally intelligent systems and is completely absent in their
smartest artificial analogs? Can we build AI systems that could be curious
about something, systems that would have an intrinsic motivation to learn? Is
such a motivation quantifiable? Is it implementable? I will discuss this
problem from the standpoint of physics. The relationship between physics and
intelligence is a consequence of the fact that correctly predicted information
is nothing but an energy resource, and the process of thinking can be viewed as
a process of accumulating and spending this resource through the acts of
perception and, respectively, decision making. The natural motivation of any
autonomous system to keep this accumulation/spending balance as high as
possible allows one to treat the problem of describing the dynamics of thinking
processes as a resource optimization problem. Here I will propose and discuss a
simple theoretical model of such an autonomous system which I call the
Autonomous Turing Machine (ATM). The potential attractiveness of ATM lies in
the fact that it is the model of a self-propelled AI for which the only
available energy resource is the information itself. For ATM, the problem of
optimal thinking, learning, and decision-making becomes conceptually simple and
mathematically well tractable. This circumstance makes the ATM an ideal
playground for studying the dynamics of intelligent behavior and allows one to
quantify many seemingly unquantifiable features of genuine intelligence.

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:
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.