Multi-Agent Cooperation and the Emergence of (Natural) Language
The current mainstream approach to train natural language systems is to expose them to large amounts of text. This passive learning is problematic if we are in- terested in developing interactive machines, such as conversational agents. We propose a framework for language learning that relies on multi-agent communi- cation. We study this learning in the context of referential games. In these games, a sender and a receiver see a pair of images. The sender is told one of them is the target and is allowed to send a message to the receiver, while the receiver must rely on it to identify the target. Thus, the agents develop their own language interactively out of the need to communicate. We show that two networks with simple configurations are able to learn to coordinate in the referential game. We further explore whether the “word meanings” induced in the game reflect intuitive semantic properties of the objects depicted in the image, and we present a simple strategy for grounding the agents’ code into natural language, a necessary step in developing machines that should eventually be able to communicate with humans.
To train natural language systems by putting multiple agents within an interactive referential communication game is very nice. As the authors mention, there has been some (although seemingly not much) previous work on using multi-agent games to teach communication, and it certainly seems like a direction worth pursuing. Moreover, the approach of switching between these games and some supervised learning, as in the experiment described in Section 5 and suggested in Section 6, seems particularly fruitful. Note: For “clarity”, I believe some of the network connections in Fig 1 have been omitted. However, given the rather highly-customized architecture and the slightly hard-to-follow description in Section 3, the shorthand diagram only adds to the confusion. The diagram probably needs to be fine-tuned, but at the very least (especially if I am misunderstanding it!), a caption must [still] be added to help the reader interpret the figure. Overall, the framework (Section 2) is great and seems quite effective/useful in various ways, the results are reasonable, and I expect there will be some interesting future variations on this work as well. Caveat: While I am quite confident I understood the paper (as per confidence score below), I do not feel I am sufficiently familiar with the most closely related literature to accurately assess the place of this work within that context.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Multi-Agent Cooperation and the Emergence of (Natural) Language
The current mainstream approach to train natural language systems is to expose them to large amounts of text. This passive learning is problematic if we are in- terested in developing interactive machines, such as conversational agents. We propose a framework for language learning that relies on multi-agent communi- cation. We study this learning in the context of referential games. In these games, a sender and a receiver see a pair of images. The sender is told one of them is the target and is allowed to send a message to the receiver, while the receiver must rely on it to identify the target. Thus, the agents develop their own language interactively out of the need to communicate. We show that two networks with simple configurations are able to learn to coordinate in the referential game. We further explore whether the “word meanings” induced in the game reflect intuitive semantic properties of the objects depicted in the image, and we present a simple strategy for grounding the agents’ code into natural language, a necessary step in developing machines that should eventually be able to communicate with humans.
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>



To train natural language systems by putting multiple agents within an interactive referential communication game is very nice. As the authors mention, there has been some (although seemingly not much) previous work on using multi-agent games to teach communication, and it certainly seems like a direction worth pursuing. Moreover, the approach of switching between these games and some supervised learning, as in the experiment described in Section 5 and suggested in Section 6, seems particularly fruitful. Note: For “clarity”, I believe some of the network connections in Fig 1 have been omitted. However, given the rather highly-customized architecture and the slightly hard-to-follow description in Section 3, the shorthand diagram only adds to the confusion. The diagram probably needs to be fine-tuned, but at the very least (especially if I am misunderstanding it!), a caption must [still] be added to help the reader interpret the figure. Overall, the framework (Section 2) is great and seems quite effective/useful in various ways, the results are reasonable, and I expect there will be some interesting future variations on this work as well. Caveat: While I am quite confident I understood the paper (as per confidence score below), I do not feel I am sufficiently familiar with the most closely related literature to accurately assess the place of this work within that context.
assistant
To train natural language systems by putting multiple agents within an interactive referential communication game is very nice. As the authors mention, there has been some (although seemingly not much) previous work on using multi-agent games to teach communication, and it certainly seems like a direction worth pursuing. Moreover, the approach of switching between these games and some supervised learning, as in the experiment described in Section 5 and suggested in Section 6, seems particularly fruitful. Note: For “clarity”, I believe some of the network connections in Fig 1 have been omitted. However, given the rather highly-customized architecture and the slightly hard-to-follow description in Section 3, the shorthand diagram only adds to the confusion. The diagram probably needs to be fine-tuned, but at the very least (especially if I am misunderstanding it! ), a caption must [still] be added to help the reader interpret the figure. Overall, the framework (Section 2) is great and seems quite effective/useful in various ways, the results are reasonable, and I expect there will be some interesting future variations on this work as well. Caveat: While I am quite confident I understood the paper (as per confidence score below), I do not feel I am sufficiently familiar with the most closely related literature to accurately assess the place of this work within that context.