Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: The current manuscript proposes to study the emergence of language when agents communicate with each other about generalizations over a set of objects, as part of a referential game. Experiments with object sets yield improved and more interpretable language that evolves between the agents. Additionally, the work also explores finding approximate logical operators in the emergent language.
main_review: **Strengths**  
(S1) The manuscript does a good job of clearly stating the hypothesis about analyzing emergent language between agents in referential games about sets of objects. The paper is well-written and easy to understand. I thoroughly enjoyed reading the paper.

(S2) Experiments are well-rounded and back up claims introduced earlier in the paper. Further, analysis of emergent language through sunburst visualizations is insightful to understand the distribution of tokens. Though ambitious, Sec 7.1 to approximately search for operations in the emergence language is an interesting read. Kudos to the authors for attempting it.

**Weaknesses**  
(W1) The paper seems to be missing a related work section. I urge the authors to include discussions around prior work related to other environmental pressures, placing this work in the context.

**Comments**  
(C1) Are there any linguistic connections as to why generalizing to a set of objects results in more systematic and interpretable (as defined by metrics in the paper) emergent language?

(C2) How does the behavior of the agents and the emergent language change if the number of attributes and attribute values increases? The first dataset has multiple valued attributes but limited (5 shapes x 6 colors), whereas the second dataset has boolean attributes. Any insights on what happens if there are N attributes where each of them take M values, with increasing N and M?

limitations_and_societal_impact: The work briefly mentions limitations (in conclusion) and broader impact.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: In this paper, the authors point the limitation of existing reference games and propose different games that require communicating generalizations over sets of objects. In the set reference game, a teacher must communicate to a student a group of objects belonging to a concept. The concept game is more challenging as each agent sees different examples of the concept. The authors also build a teacher model and a student model to play these games. They build two tasks to examine the languages developed for their proposed communication games. 



main_review: It is good that the authors propose a new reference game that requires communicating generalizations over sets of objects.

The paper is well written and easy to follow.

The authors propose several metrics to analyze the systematicity and interpretability of the learned languages.

The experiments are good. The authors test the accuracy, conditional entropy of messages given concepts, and adjusted mutual information score of different tasks.

It seems the tasks are easy and the concepts/words/language that appeared in different games are limited. It is hard to verify whether the conclusion obtained from the experiments applies to more challenging tasks. For example, does the conclusion also suitable for more challenging object categories, i.e. dogs, cats, fruits, etc., or more challenging tasks, i.e. tasks in 3D environments.
limitations_and_societal_impact: The contribution is limited. The authors propose several new reference games and evaluation metrics. However, the proposed games seem very simple, even the Concept game seems more challenging than the Setref and Ref games. 

It is obvious if the task is about high-level concept prediction, the models tend to use fewer tokens and the tokens are more consistent. But it will lose the detailed information. If the task is about low-level concept prediction, the models tend to use more tokens and capture more details. These seem very obvious and I am not sure why designing such tasks is a big contribution. It is more convincing if the authors can perform their experiments and get corresponding conclusions on more challenging tasks or environments.

The compositional experiment is interesting, but again the concepts/messages/tokens are very limited. What if the concepts need to be described by a long message? What if the difference between the two concepts is very small? Given the powerful natural networks used in recent works, it might be good to have more challenging tasks or environments.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: In the referential game setup that is widely used in the emergent communication literature, this paper proposes expanding the target into a target set, and studies the effects of different choices for the distractor and target sets. The authors find that choosing suitable sets leads to more consistent communication protocols and higher topographic similarity between concept and message spaces, but at the same time causes a drop in communicative success.

The other contribution of the paper is a composition-based evaluation of the protocols that emerge from the set-based training. Small transformers are trained to mimic operators such as ‘AND’ and ‘NOT’. Evaluating the listener performance on the messages produced by those operator models shows a non-trivial communication performance, albeit significantly reduced relative to the original speaker models. The authors conclude that there is limited evidence for compositionality in the communication protocols based on this experiment.

main_review: The method presented here is very reminiscent of, and indeed seems to be inspired by, the prototype networks technique from Snell et al. (NeurIPS 2017). In contrast to Snell et al., the authors here use a discrete intermediate representation (the message) and inspect and analyse its properties. It is hard to compare the accuracies because the datasets are different, and although that is not the focus of the authors here, it would help to more clearly place this work in the context of the existing literature.

The authors’ focus is the systematicity of the communication protocol, and how that is affected by the support and distractor sets chosen. The main finding, loosely paraphrasable as ‘more systematic information in the support and distractor sets leads to more systematic communication’ is nice to see, if not altogether surprising (e.g. Snell et al. also observe that training with more variation in the distractor set leads to better classification performance, which can be attributed to the same effect).

The search for operators that combine elementary concepts into composite ones is interesting. I salute the authors for including that section even though it does not contain very clearly positive results - putting the idea into writing, suggesting the technique, and publishing a somewhat neutral finding, are helpful contributions.

The significant drops in communicative success with sets or concept sets are unfortunate. If more systematic communication does not lead to more accurate performance (also on unseen stimuli), it might not be something to strive for in agents, in spite of the human bias in favor. I do wonder to what extent the model architecture and the complexity of the datasets can be at fault here. More inspection of the effects that various choices for the support and distractor sets have on the communicative success would make the paper more relevant for future research. The main reason for wanting systematic communication is the expected benefit for generalization. If that benefit does not materialize, the systematicity of the communication protocol becomes less interesting altogether.

All in all, while I believe the paper contains a number of interesting contributions, a big question is left open, with significant implications for the relevance of the paper. The paper has lots of interesting analysis, and I would love to see the same rigor applied to the accuracy drop question.

limitations_and_societal_impact: See main review; there is ample attention for and recognition of the limitations and impact.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: This paper explores language emergence properties through two aspects:
 - extending the classic Lewis Discrimination Game to a multi-reference/concept one. 
 - probing language compositionality with a new protocol called ACRe evaluation	

Overall, it is a well-designed paper. It slowly introduces the concept and detail the intuition. I greatly appreciate the didactic aspects of the paper.
 Experiments look sounds, and there is a nice balance between quantitative and qualitative results. 
Some visualizations -- without being ground-breaking -- are novel and help understanding communication protocols.

The paper does not have substantial weaknesses. Although the ideas are simple (they slightly extend classic ideas), they are correctly explored, and there is no overclaim.
My main concern may be that further work could have been done to bridge these ideas to the ML literature.
 Indeed, the Lewis Game extension is a form of negative sampling (which recently (re)boom with CPC) 
ACRe is a naive approach for language automata.
As those ideas already in the literature, the contribution sometimes feels a but too incremental.
Therefore, correctly bridging those concepts would be an actual core contribution that is lacking in the community.

 In the end, I enjoy the paper, and it may nicely pile up in the emergent communication literature.
 Yet, it misses this je-ne-sais-quoi that could make it excellent.
 I thus lean toward acceptance (but not clear acceptance).
main_review: As mentioned, there are no apparent flaws in this paper, so I do not have strong remarks.

 A) Could you detail further the link between the game and different fields of the ML. For instance:
  - The two games can be seen as negative sampling approaches [1]
  - The reference set could be compared to weakly supervised learning (multiple-instance learning) [2-3]
 - How ACRe compare as a language automata [4-5]

Please, if you disagree with some of these comparisons, can you elaborate further? Note those are not extensive cites.

B) l70: the sentence with label $\hat{y}_iˆS$ creates a minor ambiguity as I thought It was a classification task for a few moments

C) l120 ”hypergeometric of randomness”… sounds clever, but it is pretty obscure :) Maybe something like a non-uniform distribution with some details about this non-uniformity would be more precise.

D) In evaluation, can you detail a bit further the accuracy metric. How many positive concepts, images do you have in average. Do you compute the mean accuracy or a success ratio (all concepts are found). 

E) In tables,  please use floating numbers as the std accuracy is $10^-1$

F) Can you define further “systematic” communication. There is room for ambiguity.

G) When probing compositionality? Is there some specific reasons you did not implement TRE? Besides, why only computing BLEU1 as there are multiple characters in a message. Would you mind adding B2 at a bare minimum? Differently, 10% test hold may look a bit small. Did you try to have hard held-out composition (in the same spirit as [6])

[1] Kalantidis, Yannis, et al. "Hard negative mixing for contrastive learning." arXiv preprint arXiv:2010.01028 (2020).
[2] Song, Jiaming, and Stefano Ermon. "Multi-label contrastive predictive coding." arXiv preprint arXiv:2007.09852 (2020).
[3] Carbonneau, Marc-André, et al. "Multiple instance learning: A survey of problem characteristics and applications." Pattern Recognition 77 (2018): 329-353.
[4] Languages, Automata, and Logic 	
[5] Chapter 2 Languages and Automata - Inria
[6] Cogswell, Michael, et al. "Emergence of compositional language with deep generational transmission." arXiv preprint arXiv:1904.09067 (2019).


limitations_and_societal_impact: None
review_title: null

Meta-review: This work points out limitations of existing reference games, and proposes using communication for generalization over (concept-level) target sets instead of single targets. Different types of targets, distractors and target sets are explored. The reviewers agree the current work presents several good and useful contributions to the field of emergent language. The authors did an excellent job in addressing any remaining concerns in the rebuttal.
Decision: Accept (Poster)