Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: Teaching agents to play lewis signalling games is still one of the most common ways of studying emergent communication, and has historically been a test bed for our understanding of many EC dynamics.  In this paper, the authors propose to view the standard lewis game loss as a composition of two terms -- one that essentially promotes a unique message for each object, and one that promotes mutual understandability between speaker and listener.  It is this second term that is the main focus here, deemed as co-adaptation, as the authors explore the effect that artificially controlling this factor has on the learned languages.  They find that when trained in the de facto way, this term often overfits, and this overfitting has a detrimental effect on compositionality.

strengths_and_weaknesses: Strengths:

First off, many thanks to the authors for presenting such a clear paper.  It was generally a joy to read.  The proposed decomposition makes a lot of sense, and maybe more than that just formalizing the loss in this way may help other researchers to structure the hypotheses of their own work.  In particular I liked the formalization of l^train_adapt and its role in the loss function since this gets quite close I think to how one should discuss generalization to novel object-attribute combinations through compositionality.

In terms of the results, the authors make a compelling case for the relationship between co-adaptation overfitting hindering compositionality in languages.  Both experiments with really hands-on early stopping regularization, and with more traditional ML regularization on the co-adaptation term, yield better generalization.


Weaknesses:

Although I think this paper is mostly ready for publication in its current state, and I do think it is beneficial to the EC community, I'm not sure the scope of the contribution is significant enough to warrant publication at NeurIPS, both in terms of what benefit it brings to the EC community, and in terms of it's interest to the larger set of research at the venue.  I think the contribution is quite niche (okay) and maybe rehashing a lot of what the EC community seems to know.

Does this work really shine new light on these issues, or does it just reinforce the lessons learned by the slew of other EC work?  I think it comes down to this: other (cited) EC work tends to also tackle the same problem of lack of compositionality stemming from co-adaptation, but focus more on how solutions may emerge from population dynamics and which sets of agents are communicating with each other.  These will ultimately find themselves expressed in gradients.  In this work, the authors cut straight through to the optimization itself.  In that sense, it helps point research towards more desirable properties in the loss function, but it is still up to the research community to find plausible ways for these pressures to originate.  Because presumably as a model of language development we don't want strong assumptions governing the loss itself over the actual agent/environment dynamics?  And the particular method of regularizing as applied here (primarily the early stopping) seems hacky and more for the purpose of probing than for simulation, whereas other proposed methods like neural iterated learning apply similar constraints in a more plausible way.

So in that extent, the closing argument (L342) seems somewhat backwards to me -- researchers seem aware that co-adaptation and lack of compositionality is undesirable, so they design realistic and intuitive pressures which ultimately have similar effects on the loss.  It is not surprising if a more hands-on approach goes directly to the loss to accomplish the same effect - as the authors say, "remarkably, this emergent compositionality does not result from [things]..." but it is instead created forcefully, and not in some natural way that would be remarkable IMO.

In conclusion, like a good literature review, I would like to see it published - it could be useful to EC researchers.  But I find it difficult to see this particular analysis as sufficiently novel or important to future EC research.
questions: 
I think the authors did a convincing job at showing how this co-adaptability effect gets to the heart of effective communication protocols failing to exhibit compositional structure and generalization, so as a take-away I think that is constant, but I'm unsure from a practical perspective the extent to which it matters to regularize the term explicitly vs. obtain regularization from aspects of the game/environment.

To this end, to what extent do these results persist as the task changes?  I was a bit disappointed that in scaling it up to the vision task, the size of the task was deliberately kept low.  Is it difficult to run on the full set?  Could the authors present some results more in-line with what other EC work is focusing on?  Perhaps it is not important to consider ad hoc regularization of the loss function if the environment is sufficiently rich or the task sufficiently hard.

Albeit the decomposition and analysis are novel, but can the authors clarify how their focus on the co-adaptability factor and early stopping regularization is different from the goal and solution of neural iterated learning and ease of teaching (both cited)?  It seems that these papers have identified this dynamic as a point where compositionality and generalization fail.
limitations: In terms of limitations, not sufficiently.  In terms of societal impact, it should not be applicable.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: The authors propose a framework for studying the generalization and compositionality of communication games playing Lewis signaling games. In short, there are two agents a speaker and a listener and "the speaker observes a random state from its environment, e.g. an image, and sends a signal to the listener. The listener then undertakes an action based on this signal. Finally, both agents are equally rewarded based on the outcome of the listener’s action." (Line 24-26) The authors demonstrate how the agent's loss can be decomposed into an information term that quantifies the degree of ambiguity of the language protocol and a co-adaptation term that quantifies the gap between the listener and its optimum. 

This observation allows studying the source of overfitting (or lack of generalization) in the communication agents especially when communication is in an unseen state from the training set. The main idea is to decrease the importance of the co-adaptation term in the loss so as to avoid the speaker to overfit the listener. For doing so, the authors experimented with using different training strategies Continuous listener, Partial listener, and Early stopping listener (Section 3.3). 

The authors experimented using, the EGG toolkit with an environment in which every object has K = 6 attributes, each taking 10 different values, for a total of 1 million objects, and a non-overlapping train/val/test split is used. The experiments confirm the hypothesis of the loss decomposition by showing the importance of the co-adaptation term while evaluating an unseen set of objects (from the test set). Moreover, the authors show the effectiveness of the proposed method on more complex games CelebA and ImageNet, showing a much stronger generalization compared to a "standard" Continuous listener.
strengths_and_weaknesses: Strengths
- The proposed loss decomposition is novel, to the best of my knowledge. The paper is formal and rigorous without being too verbose or hard to read in its presentation.
- The experimental results are conclusive and well-conducted. Adding the final experiments on more complex tasks adds validation to the proposed hypothesis. 
- The paper is very well written and easy to follow. The content is well organized and the plots add to the presentation.
questions: I have no particular question. The paper is very clear and self-explanatory, I appreciated the read.
limitations: Not reported by the authors.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: The paper provides a theoretical analysis of the objective of Lewis communication games, that reveal that the objective can be decomposed into two components: (1) mutual-information component, that encourages the speaker to generate unambiguous (highly informative) messages on its input; and (2) adaptation component, that encourages the distribution of the speaker's posterior to match the distribution of the listener. The latter is directly optimized by both players, and is zeroed when the listener achieves an optimal distribution over objects given a message. Following this theoretical analysis, the authors hypothesize that overfitting issues in the adaptation game result in some previously-reported problems in the emergent communication, such as low accuracy and lack of compositionality.

To test this hypothesis, the co adaption term is estimated with a probe -- a listener that is trained to convergence over the frozen speaker. By modifying the weight of the probe's loss and the original loss in the reward of the speaker, the influence of mutual information component vs. the adaption component is tested. it is shown that (1) the adaptation term severely overfits, while the information term does not (2) by replacing the continuous co-training of the speaker and the listener with an alternate training where the listener is trained for N steps, it is possible to find a setting where the emergent language presents much superior properties. 
strengths_and_weaknesses: Strengths: the paper is an excellent example of a simple and highly elegant theoretical observation that leads to significant empirical gains. It is largely clearly written (apart from some issues, see below), and the empirical results (especially figure 2 and 3) are persuasive and surprising -- they point out to an inherent limitation that arises from optimization and may explain many of the previously reported issues with neural Lewis games. As such, the analysis in this paper has the potential of substantially influencing the study of emergent communication, and I strongly support its acceptance. 

Weaknesses: while the presentation is largely clear, it would be beneficial to improve, in the camera ready version, the presentation of the experiment that involves the weighting of the probe's loss and the original loss. In equations 10-11 the constant alpha is presented as a way to *upweight* the original loss, while, in practice, it is used to *downweight* the influence of the adaptation term. While the two are equivalent, these two conflicting views make it harder to understand the discussion in lines 237-244 (I think this paragraph in particular has to be improved. particularly the sentence at line 240). 
questions: - is it accurate to say that the proposed weighting scheme can be equivalently describes as follows? (1) we know the overall value of the loss L, and we can estimate the information term L_info (which doesn't require marginalization) with a probe; (2) Then the adaptation term is approximated as L_adapt = L - L_info; (3) Instead of training with L, we train with alpha*L_adapt + (1-alpha) * L_info.  In case this description is correct, I think it's a more intuitive way to describe the weighting experiment.

- Why does the adaptation term strongly overfit, while the other term does not?

- It would be beneficial to briefly explain why the speaker's posterior is the optimal distribution over objects for the listener. Intuitively, why can't the listener make up for some ambiguity in the speaker's messages by changing its distribution?
limitations: Yes.
review_title: null

Meta-review: metareview: This paper shows that the objective for Lewis games, as treated in the recent emergent communication (EC) literature, can be decomposed into two losses: an information loss (whether each message refers to a unique referent) and a co-adaptation loss (which quantifies the alignment of the speaker and listener). It shows that lack of generalization in unregularized EC is mostly due to the latter, and empirically shows that intervening on co-adaptation via regularization (early stopping/reinitialization) improves generalization.

The reviewers are generally positive about this paper. The one somewhat negative score is actually quite positive as well: the reviewer concedes that “this is a solid paper and deserves to be accepted even in its current state, at some venue”, but questions the overall impact of the contribution and whether it merits publication in NeurIPS. Unfortunately a fourth review, although promised by the reviewer, did not materialize in time, even after repeated prodding, so I had to provide it myself (see below).

As an area chair, I am somewhat torn about this paper. It is well-written and I think the field will benefit from having these things made more explicit. Sadly, I also think it shows very clearly how frustratingly little progress EC has made as a field. We are still talking about the same things, years later after its revival, in uninteresting unrealistic toy settings looking for “linguistics” and “compositionality” when basic information/communication theory combined with basic optimization would probably be more adequate. Overall, I think this paper can help the field move in the right direction and I am recommending conditional acceptance: the notation needs to be shored up, the assumptions and limitations need to be made much more explicit, and it needs to be made suitable not only for readers intricately familiar with EC, but also for readers who are only just reading their first paper on the subject.

—

More detailed AC review:

Strengths:
* Presentation: This paper is well-written, the decomposition makes a lot of sense and will not come as a surprise to anyone working in the field.
* Soundness: The experiments and evaluation are thorough and appear to be easily reproducible given the details provided. The paper provides additional experiments on different more “complex games” to overcome potential criticisms of its toy task nature.
* Impact: The EC literature, or even the field of EC broadly speaking, is extremely troubled by a poor understanding of how basic assumptions (eg questions of optimization, setup) impact observations (eg compositionality) and there is an extraordinary amount of wheel-reinvention, exacerbated by the lack of a standardized evaluation protocol. This paper has the potential to help practitioners be more explicit about their assumptions.

Weaknesses:
* Applicability and novelty: The main contribution of this paper, in my mind, is showing the decomposition and using it to elucidate the impact of training dynamics on the emerging communication protocol. However, this decomposition only applies in a limited setting and that assumption is not nearly made explicit enough. The final loss is negative log likelihood and the speaker is trained via policy gradients, without any constraints or regularization (either on the communication channel or the listener supervision). Almost all papers in EC are exactly about what constraints/regularization/dynamics we can impose in order to obtain better generalization. I think the distinction (i.e. “decomposition”) between decodability (“information”) and learnability (“adaptation”) is well-known amongst serious EC practitioners, and almost any paper published on the subject deals with these in some form.
* Clarity: Given the above observation, and the fact that the decomposition follows from trivial math, the exposition itself is valuable if it makes something very explicit that was heretofore not explicit enough, such that future work will benefit from it being explicit. The paper has the potential to do this, but in my opinion, disappointingly, falls short: too much of the writing assumes too much prior knowledge on the part of the reader. For this paper to be maximally valuable, I would want it to be understandable by someone who doesn’t know anything about emergent communication and reads this as their very first paper. This issue is particularly prevalent in the most important part, Section 2 and the corresponding appendix: the notation in the proofs is almost unforgivably convoluted; the sub- and super-script mixing for different parameterizations is unnecessarily confusing, especially with the listener parameterization \phi never actually being introduced as such; the actual proofs in the appendix never making explicit that the two losses concern the speaker and listener respectively (try having this read by someone unfamiliar with the field, they’d instantly be lost); and all of it is basically building on the work from a very specific group of people who do things in a very particular way using a framework (EGG) that nobody else uses, without ever making explicit that other EC papers do things very differently (there are definitely EC papers that do early stopping, freezing/probing in different phases of training, etc. but they tend to study what constraints can be imposed on top of the standard task formulation to make things generalize, usually in much more sophisticated games). I also did not like that the communication channel itself was not bottlenecked, with |V|=T=10 — in this setting everything collapses to basic information theory, and the assumption is not realistic for studying any emergent linguistic phenomena.

Overall, if it was up to me, I would have written this paper very differently, with an argument along the lines of: the EC literature is a mess, let’s make things more explicit by decomposing the loss in the most basic EC setting, and then we can understand all of the interventions in the prior EC literature (eg ensembles/freezing/populations/grounding/constraints/regularizations/etc) in this new light, and we can even come up with some new approaches to tackling this problem, such as down-weighting co-adaptation. I think the paper has merit, and I think it can be accepted but it really has to address its shortcomings: Section 2 and the proofs have to be notationally extremely clear with the proofs written out in much more detail and the paper has to make its assumptions much more explicit, i.e. that it applies to the limited basic setting that was mostly studied by a very limited group of people.

Typos I came across:
- “and experimental choice” - choices
- “to ease the reader intuition” - reader’s
- “deep model are large enough” - models
- “the train co-adapation keeps dismishing” - co-adaptation, diminishing
confidence: Certain
Decision: Accept