Emergent Communication in a Multi-Modal, Multi-Step Referential Game
Inspired by previous work on emergent communication in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration. The multi-modal multi-step setting allows agents to develop an internal communication significantly closer to natural language, in that they share a single set of messages, and that the length of the conversation may vary according to the difficulty of the task. We examine these properties empirically using a dataset consisting of images and textual descriptions of mammals, where the agents are tasked with identifying the correct object. Our experiments indicate that a robust and efficient communication protocol emerges, where gradual information exchange informs better predictions and higher communication bandwidth improves generalization.
 The setup in the paper for learning representations is different to many other approaches in the area, using to agents that communicate over descriptions of objects using different modalities. The experimental setup is interesting in that it allows comparing approaches in learning an effective representation. The paper does mention the agents will be available, but leaves open wether the dataset will be also available. For reproducibility and comparisons, this availability would be essential. I like that the paper gives a bit of context, but presentation of results could be clearer, and I am missing some more explicit information on training and results (eg how long / how many training examples, how many testing, classification rates, etc). The paper says is the training procedure is described in Appendix A, but as far as I see that contains the table of notations.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Emergent Communication in a Multi-Modal, Multi-Step Referential Game
Inspired by previous work on emergent communication in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration. The multi-modal multi-step setting allows agents to develop an internal communication significantly closer to natural language, in that they share a single set of messages, and that the length of the conversation may vary according to the difficulty of the task. We examine these properties empirically using a dataset consisting of images and textual descriptions of mammals, where the agents are tasked with identifying the correct object. Our experiments indicate that a robust and efficient communication protocol emerges, where gradual information exchange informs better predictions and higher communication bandwidth improves generalization.
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 setup in the paper for learning representations is different to many other approaches in the area, using to agents that communicate over descriptions of objects using different modalities. The experimental setup is interesting in that it allows comparing approaches in learning an effective representation. The paper does mention the agents will be available, but leaves open wether the dataset will be also available. For reproducibility and comparisons, this availability would be essential. I like that the paper gives a bit of context, but presentation of results could be clearer, and I am missing some more explicit information on training and results (eg how long / how many training examples, how many testing, classification rates, etc). The paper says is the training procedure is described in Appendix A, but as far as I see that contains the table of notations.
assistant
 The setup in the paper for learning representations is different to many other approaches in the area, using to agents that communicate over descriptions of objects using different modalities. The experimental setup is interesting in that it allows comparing approaches in learning an effective representation. The paper does mention the agents will be available, but leaves open wether the dataset will be also available. For reproducibility and comparisons, this availability would be essential. I like that the paper gives a bit of context, but presentation of results could be clearer, and I am missing some more explicit information on training and results (eg how long / how many training examples, how many testing, classification rates, etc). The paper says is the training procedure is described in Appendix A, but as far as I see that contains the table of notations.