Associative Conversation Model: Generating Visual Information from Textual Information
In this paper, we propose the Associative Conversation Model that generates visual information from textual information and uses it for generating sentences in order to utilize visual information in a dialogue system without image input. In research on Neural Machine Translation, there are studies that generate translated sentences using both images and sentences, and these studies show that visual information improves translation performance. However, it is not possible to use sentence generation algorithms using images for the dialogue systems since many text-based dialogue systems only accept text input. Our approach generates (associates) visual information from input text and generates response text using context vector fusing associative visual information and sentence textual information. A comparative experiment between our proposed model and a model without association showed that our proposed model is generating useful sentences by associating visual information related to sentences. Furthermore, analysis experiment of visual association showed that our proposed model generates (associates) visual information effective for sentence generation.
 The authors describe a method to be used in text dialogue systems. The contribution of the paper relies on the usage of visual information to enhance the performance of a dialogue system. An input phrase is expanded with visual information (visual context vectors), next visual and textual information is merged in a deep model that provides the final answer. Although the idea is interesting, I found the methodology fairly straightforward (only using known models in each step) and not having a particular component as contribution. What is more, the experimental evaluation is limited and purely qualitative: a few examples are provided. Also, I am not convinced on the evaluation protocol: the authors used captions from video+videos as input data, and used "the next caption" as the response of the dialogue system. I am not very familiar with this type of systems, but it is clear to me that the evaluation is biased and does not prove the working hypothesis of the authors. Finally, no comparison with related works is provided. English needs to be polished
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Associative Conversation Model: Generating Visual Information from Textual Information
In this paper, we propose the Associative Conversation Model that generates visual information from textual information and uses it for generating sentences in order to utilize visual information in a dialogue system without image input. In research on Neural Machine Translation, there are studies that generate translated sentences using both images and sentences, and these studies show that visual information improves translation performance. However, it is not possible to use sentence generation algorithms using images for the dialogue systems since many text-based dialogue systems only accept text input. Our approach generates (associates) visual information from input text and generates response text using context vector fusing associative visual information and sentence textual information. A comparative experiment between our proposed model and a model without association showed that our proposed model is generating useful sentences by associating visual information related to sentences. Furthermore, analysis experiment of visual association showed that our proposed model generates (associates) visual information effective for sentence generation.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The authors describe a method to be used in text dialogue systems. The contribution of the paper relies on the usage of visual information to enhance the performance of a dialogue system. An input phrase is expanded with visual information (visual context vectors), next visual and textual information is merged in a deep model that provides the final answer. Although the idea is interesting, I found the methodology fairly straightforward (only using known models in each step) and not having a particular component as contribution. What is more, the experimental evaluation is limited and purely qualitative: a few examples are provided. Also, I am not convinced on the evaluation protocol: the authors used captions from video+videos as input data, and used "the next caption" as the response of the dialogue system. I am not very familiar with this type of systems, but it is clear to me that the evaluation is biased and does not prove the working hypothesis of the authors. Finally, no comparison with related works is provided. English needs to be polished
assistant
 The authors describe a method to be used in text dialogue systems. The contribution of the paper relies on the usage of visual information to enhance the performance of a dialogue system. An input phrase is expanded with visual information (visual context vectors), next visual and textual information is merged in a deep model that provides the final answer. Although the idea is interesting, I found the methodology fairly straightforward (only using known models in each step) and not having a particular component as contribution. What is more, the experimental evaluation is limited and purely qualitative: a few examples are provided. Also, I am not convinced on the evaluation protocol: the authors used captions from video+videos as input data, and used "the next caption" as the response of the dialogue system. I am not very familiar with this type of systems, but it is clear to me that the evaluation is biased and does not prove the working hypothesis of the authors. Finally, no comparison with related works is provided. English needs to be polished