Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: This paper studies population size and connectivity graph effects in emergent communication in a population of agents. By limiting the communication partners in specific ways, i.e. deleting edges from the fully connected graph, the authors show that dialects can evolve: communication protocols that are specific to sub-groups even when those groups are not completely isolated. Furthermore, by experimenting with different connectivity patterns that share a constant overall graph sparsity, they find that a pattern with few highly connected nodes (hubs) leads to significantly higher communication success than random or uniform patterns. Such hubs are a common feature of complex networks found in many natural systems. In terms of communicative success, the authors find that more complex tasks are associated with better generalization to unseen data (a smaller drop in success relative to the training data)

The paper also introduces a variant of the widely used referential game where the speaker and listener observe non-overlapping aspects of the images they communicate about, and where agents act as both listener and speaker, depending on how they are sampled at the start of a round in the game.

main_review: The signalling game variant proposed in the paper is interesting, in that it has different information requirements than the more common fully observed one. The speaker does not know the target exactly, and has to communicate to the listener which images are candidates. One aspect that did not become obvious to me while reading the paper is whether the listener also receives the instruction, or whether the speaker has to communicate that too.

To better understand the contribution of the partial observation, it would be good to ablate it by replicating some of the experiments in a fully observed signalling game, and investigating whether the resulting language patterns are at all different. A priori I don’t see a very clear reason why they should be, but I would be happy to be proven wrong on this point.

The graph configuration effects are nice results. The emergence of dialects even in the absence of clear subgroups (with higher internal connectivity than external) like in figures 4b and 4c is even somewhat surprising. The optimal graph question is a valuable contribution the field of multi-agent communication, as it has the potential to enable a significant saving in the computational cost of such experiments.

One question that is left open, but would be very interesting and probably relatively straightforward to answer, is what happens to the optimal graph structure when the constraint that each node has at least three outgoing vertices is dropped. The result found here, with the hub nodes, follows one pattern that often occurs in natural complex systems, but goes against another - that of the power law distribution. I would have expected to see the power law here too; can you comment on why it does not emerge?

The results you find are mostly about communicative success, but within the population that the agents are trained with. I would really like to know whether the languages that are formed in such non-uniform connectivity graphs have the same properties as those from uniform graphs when it comes to compositionality and learnability. E.g. analysing the topographic similarity of the messages to the ground truth feature vectors could provide some insights here, as could training newly initialized evaluation agents against members of a fully trained population.

A third way to make the paper stronger would be the inclusion of more standard datasets, to enable easier comparisons to existing work.

All in all, I believe this paper takes on a very interesting and relevant question, but is a little light on the results presented.
limitations_and_societal_impact: Yes, section 6 and appendix G.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The paper studies the effect of population size and graph connectivity on the emergence of communication. The main questions studied in the paper were 1) how does the size of agents' population affect task success? and 2) when agents don't get to train directly, but through some other agents, how well they can communicate with each other when they put in contact? The game that was used in this paper to study the problem, was a referential game, and the task was discrimination task. The agents can take 2 roles of speaker and listener, such that speaker gets some observation and send a message to listener, and listener gets some other observation along with speaker's message, and it has to make a decision in a discriminative setup. 
main_review: The paper is well-written and well-organised. 

The problem understudy is an interesting and can be an impactful problem to study. The idea of the paper is not novel and it was studied in previous papers. Also, the game that was designed is a referential game that you can find very similar games in other emergent communication papers.

I found the methodology used to solve the problem, i.e. train agents, not a very good approach! There are some weaknesses when it comes to the used methodology. One of them is centralized training! In multi-agent systems, using centralized training violates a fundamental assumption that information should not flow between agents. The other weakness is related to using a discrimination loss and treat the problem as a supervised learning rather than reinforcement learning. The discrimination loss is a very strong signal such that training such agents will be an easy task. 

limitations_and_societal_impact: I have 3 main concerns:
1. Centralized training: Although we see in the litrature that centralized training may be an acceptable approach in some cases, I believe for the problem understudied in this paper, we need to train agents independently. The authors argued that only gradients flow from listener agent to the speaker agent, and it doesn't contain information about parameter values of the other agent. I believe gradients contain information about inner state of the listener agents such that without having this information, training speaker agents may be hard! The nature of emergent communication research require independent training as the message sent by speaker shouldn't be tuned based on inner state of listener, but only by the task reward.
2. Supervised training: did you try to train agents using reinforcement learning as well?
3. Some of the conclusions are not clear to me. For example, the authors showed communication success graphs in Figure 2 and 3. Is it the results after you trained the agents and took the best performing ones? I don't know how to interpret the results shown as I believe the entire training should be shown (at each timestep). In Figure 2, the fact that population size doesn't affect communication success rate may mean all the agents in the population are copies of each other! Is that the case?
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The paper investigates the results of Emergent Communication learning in different settings (number of agents, communication connectivity, task difficulty). The authors use a reference game variant where the communicating parties are limited in their perceptual abilities and hence need to communicate in order to solve the task (select a specific object among a number of alternatives). The authors obtain a number of interesting observations (for example, observing that larger groups of agents result in more diverse communication protocols, which (in certain settings) still allow to achieve high communication success rate).

## Strengths 
- The authors tackle a highly relevant problem
- The experimental setup aims to investigate the impact of a number of factors that may indeed be crucial in determining the properties of emergent communication
- The paper has a large number of illustrations and is generally well-written, which promotes understanding.

## Weaknesses
- The decision to use early stopping, while motivated by valid concerns, does not actually address these concerns. A universally controlled amount of training in terms of total number of interaction per agent would be much easier to interpret. Ideally, more than one schedule should be tested: 1-training until convergence, 2-training for a set number of interactions for any given agent.
- Literature review overlooks a number of relevant works and thus inadvertently presents the experimental setup as more novel than it is. 
main_review: ## Originality, novelty & relevance

The paper addresses a relevant problem by providing a new take on a well-studied referential game setup.
Although the setup explored in the paper is quite close to a number of existing works, I believe that the paper expands previously known results and modifies the setup enough to be considered sufficiently novel.

## Clarity 

The paper is generally well written and is a pleasure to read. The authors also provide a number of high-quality visualizations that further promote understanding and improve the reading experience.

## Literature review

The literature review and, generally, a number of branding decisions severely limit (in my opinion) the quality of the paper.

Firstly, some quotations are not very precise. For example, authors say that they adapted a symmetric setting since it "allows all agents to function as both speakers and listeners and suppresses potential unfavored bias that might arise in the asymmetrical setting that potentially distracts our analyses on the convergence of a shared language", quoting Bouchacourt and Baroni, 2019 (https://arxiv.org/pdf/1905.11871.pdf). But the quoted material does not seem to support the claim. Here is a quote from the cited paper: "However, we also find that even perfectly symmetric agents converge to distinct idiolects instead of developing a single, shared code."

Secondly, certain phrasing decisions sound like the authors claim to introduce things that are, in fact, already thoroughly explored. For example, the authors say "we construct a unique setting within which communication among agents is indispensable for achieving their shared goals", which makes a confusing impression, since such a description is true for most (if not all) referential game settings. Similarly, on line 59, the authors phrase the symmetric nature of their game as a novelty, while it was investigated in numerous existing works.

Perhaps, the most concerning thing is that the literature review, while giving a broad overview of the field, does not discuss in enough detail the works most closely resembling the paper.

For example, the effect of population size on emergent communication outcomes was considered in
https://direct.mit.edu/isal/proceedings/isal2020/32/678/98420 

In a similar vein, the authors say "Our work is distinctive in a way that we do not assign agents into hard clusters of communities, and therefore, we observe finer granularities of linguistic continuums at agent-levels rather than at community-levels", but https://arxiv.org/abs/2007.09820 studies the effects of connectivity structure on emergent communication without hard clustering. 

Furthermore, the authors "introduce message agreement and communication success rates used to evaluate language convergence", without mentioning https://arxiv.org/abs/1903.05168 or https://direct.mit.edu/isal/proceedings/isal2020/32/678/98420 which both pay particular attention towards discussing metrics that can/should be used to study emergent communication in similar setups.

Finally, the paper repeatedly mentions that it is not injecting architectural bias. E.g. "without injecting architectural bias into the environment or agents that could favor or hamper communication, our goal is to investigate the effect of emergent communication and analyze how agents’ general agreement on the communication protocol changes under varying group settings." This claim is vague, and it seems to slightly misrepresent the situation. For example, one of the limitations of this work is that the agents are trained in a centralized manner (i.e. they "merge" during training to propagate gradients). While this decision is understandable and common, it is unrealistic for modeling real-world communication system evolution, and is an example of a bias that fosters communication learning.

Overall, the literature review helps to place the work in the global context of the field, but omits a number of works that closely resemble the present contribution. Additionally, a number of phrasing decisions are sub-optimal, giving the paper an impression of making unwarranted claims.

## Experiments

Most of the experiments are reasonable and provide insight into the problem in question. There are, however, limitations.

One of the questionable decisions is using the Early Stopping criterion. While the authors mention the problem in the appendix C3, their solution does not seem satisfactory. For example, under their schedule, when N=2, the agents stop training after 1000 epochs. At the same time, for N=128, 60000 epochs is required. On the first glance it seems that each agent in the group adds ~500 epochs to training and that each agent gets the same amount of exposure to training stimuli. But judging by the code, (multiagent_communication.py), the epoch size itself depends on the population size. As a result, agents in larger communities get exposed to a larger set of stimuli, which is crucial for generalization.

This limitation could explain the results in Figure 3. For complex tasks, agents in small communities simply learn too fast to get exposed to a large enough pool of stimuli, hence they don't generalize well.

This seems to contradict what authors wrote in lines 252-260. In particular, they say that "number of observations provided to agents during training is maintained or even decreases depending on the early stopping criteria". It's not clear how this conclusion was reached. I would appreciate a clarification on that matter.

Lastly, some experiments have unclear interpretations. For example, figure 4 claims that dialects only form in the cases with relatively large maximal communication distance. At the same time, it seems that dialects do form in all conditions, but the dialect groups are very small. Additionally, the experiment seems to contradict the claim on line 69-70. The authors state "At the same time, distant pairs of agents that have not interacted during training can still communicate.", while figure 4k strongly suggests that distant agents actually fail to communicate.

## Conclusion

The paper tackles an important problem and provides a number of interesting results. 

Many of the key experimental results, however, are either weakened by the early stopping problem or are not as clearly interpreted as authors present them (for example, the qualitative evaluation of what is happening on figure 4). 

The literature review does not (in my opinion) properly represent the paper's relation to previous work and does not properly separate novel contributions from previously known results.

At present, I select "marginally below acceptance", but I will be open to read other reviewers' opinions and the authors' rebuttal. 

I would be especially glad to be wrong when it comes to the early stopping problem (I described my concern in the "Experiments" section of the review).

## Questions
I would appreciate a comment on my concerns in the Experiments and Literature review sections.

Additionally, for how many epochs were the agents trained to obtain results in Figure 4? Was the "early stopping" criterion applied as well?

## On authors' response

I have read the responses and increased my score by 1 point. The reason I can not select "full accept" is because although authors plan to rewrite parts of the paper and tone down the claims, I can not check the quality of the final product.
limitations_and_societal_impact: The limitations were partially addressed, but I believe that the paper would benefit from expanding that section, especially when it comes to the potential confounding effects of early stopping.

There is no discussion of societal impacts, but I think it is reasonable since the paper is not addressing a question of immediate practical application.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: This paper proposes a number of experiments within a novel referential game where agents must learn to communicate among a population of agents. Authors design a novel referential game played in an artificial dataset of shapes and colors, where speaker and listener have access to a description of the true shape and color but are only able to either perceive shape or color. Thus, an agents' message must fill in the missing information of their corresponding listener, such that the listener can identify the correct target image.

With this setup, the authors design an experiment where there are a group of agents, and agents either are jointly trained with all other agents, or in more isolated connectivity settings (e.g. agents only speak to one or two other agents). Their aim is to see how the group size and connectivity of the group affects the learned communication protocol along a variety of axes:

- Q: how similar are the agents' utterances as the group size increases? (A: agents maintain communicative success, but messages become *more diverse* as the group size increases.)
- Q: Are agents able to generalize to new partners within a population, and to unseen tasks? (A: As agents learn from larger numbers of agents, zero-shot generalization to other agents also increases; agents do show some generalizability to unseen tasks, though it seems to vary based on group size and the complexity of the task distribution.)
- What is the optimal communicative graph which results in the least number of links between agents, but most homogenous communication?
main_review: Overall, this is a fairly well-written paper with a number of interesting findings on how group size effects learned communication protocols in multi-agent environments. However, I also see several weaknesses related to the odd experimental design, imprecise evaluation, lack of (discussion of) broader applicability of these experiments and comparisons to related work. Thus, right now, I'm a bit on the fence about this paper, and am hesitant to unequivocally recommend acceptance.

## Strengths

This paper has some interesting findings:

1. Perhaps the most potentially interesting is the finding that larger community sizes increase diversity of the languages (L243-244), yet maintain linguistic success. This is perhaps the opposite of what the human linguistic evolution literature might suggest, and similar papers (e.g. Tieleman et al. 2019).
2. The experiment for identifying the optimal communication graph to maintain intelligibility among the agents is quite interesting. I imagine this might have some interesting downstream uses, but it's not quite clear to me. I'd be interested in the authors thoughts on this.

## Weaknesses

### Unusual experimental design (reference game) limits comparability to past approaches.

Authors design a variant of the reference game where agents play both the role of the speaker and listener, and are divided into agents that can only perceive *shape* and those that can only perceive *color*. In the game, unlike the typical referential game, both speaker and listener agents receive an instruction which denotes the features of the target shape, though since speaker and listener are unable to identify one of the features (shape/color) this is insufficient information to solve the task from the instruction alone. Therefore presumably the speaker must convey a message that encodes which objects have the target feature it can recognize. This is less of a referential game and more of a partial-missing-information game.

I feel like this design choice is somewhat odd and insufficiently motivated. Authors claim that this setup encourages *symmetry* of the game setting, in that agents do an equal degree of speaking and listening; but this doesn't seem unique to this experimental design, and can indeed be implemented in standard Lewis referential games (we simply endow an agent with both language understanding and generation modules). Agents with dual speaker/listener roles have also been implemented in Graesser et al., 2019, Choi et al., 2018.

In the absence of any distinct advantages of the currently presented referential game setup, I have a strong prior towards using as standard of an experimental setup as possible; this concern generally falls under the broader concern about the extent to which the findings found in this paper can be generalized to other agents, environments, and datasets.

### Imprecise evaluation

Some of the evaluation in the paper is seems vague and ad-hoc, and I would have liked more standardized/systematic ways of evaluating the emergent language. A clear example is L231, the authors group messages according to criteria identified themselves, e.g. "the target object is "blue" and it is at the 3rd position in the observation." They display only 3 types of messages, but do not state what kinds of messages were identified and vaguely state that "ther are more than three types of messages we can identify." in Footnote 6.

This is all quite confusing: it's hard to understand what criteria the authors used to categorize the messages, and how the meaning of such messages was verified (how do we know that a message means "3rd position in the observation?"). Furthermore, the authors use a t-SNE plot to illustrate that it gets harder to identify which agent transmitted what messages as the group size increases. I find it really hard to examine parts of a t-SNE plot, with 3 messages labeled according to categories that are unknown to me, and conclude based on the closeness of some of the dots in the plot that "what is entailed in the message becomes more relevant when identifying the message than who is sending the message" (and even this line is a bit inscrutable to me).

Similarly the t-SNE plots in Section 5.4, Figure 4a-e apparently plot messages for color-only speakers that "have similar observations", but again this is quite vague, and the conclusion is a bit unclear to me (how does the scattered-ness of 4e illustrate the rise of "dialects"?) 

Finally, in L248, details of how the dataset tests generalization is crucial to the conclusions of this section. It's hard to reconstruct what is happening from the few sentences here ("diversify colors"). These details shouldn't be left for the Appendix.

### Artificial experiments only

Only one artificial shape dataset is explored, and the data and experimental design (as mentioned above) limit the generalizability of the present results. The dataset uses only artificial colors and shapes. Moreover, the experimental design requires cleanly dividing agents by the dataset characteristics into one of two features (color/shape)—how commonly will we encounter such a setting in practice? Do the conclusions about the regularity and systematicity of the languages based on population size extend to more tasks where it is harder to extract the relevant features (e.g. more realistic image-based reference games) or for tasks besides reference games entirely? The "simple" and "complex" tasks explored at the end of section 5.3 lack calibration - we obtain "simple tasks" by reducing the colors and shapes by one or two possibilities, but perhaps these tasks are both extremely simple in the context of other environments we might deploy our agents in. Thus, I'm hesitant to make broad conclusions that the authors do, e.g. "in complex environments, the emergent protocol generalizes better as the group size increases."

### Could use more discussion about broader impacts

There are several experimental results presented in this study, where many parameters of group communication are verified, but aside from being interesting experiments, it's somewhat unclear what the takeaways are for either people interested in linguistic evolution or people who want to build better multi-agent systems.

What are some examples we might face in practice, in multi-agent communication settings, where the communication graph of a population is not fixed, is bipartite w/ a clean division in information accessibility, etc?

The optimized communication graph experiment in section 5.5 is interesting, but it's quite unclear to me why we would want to do such a thing. For example, we find that one node emerges as "hub nodes" which regularize the communication protocol across the population. Are we meant to take away from this something about human linguistic evolution? When would we want a non-fully-connected communication graph in a multi-agent setting?

### Some overlaps and lack of comparison to related work

- Tieleman et al. Shaping representations through communication: community size effect in artificial learning systems. NeurIPS ViGIL 2019. The communication experiments with continuous messages in the authors' paper are very similar to the experiments here. Some of the conclusions of the authors should be compared/contrasted with Tieleman et al. Seems like authors conclude that languages become more discordant (but still successful) when group size increases, while Tieleman et al. almost observe the opposite: larger group sizes result in *less idosyncratic, better* learned representations.
- The Graesser et al. paper is particularly relevant; it's mentioned that there are similarities in L106, but not at all referred to for the rest of the paper, though I feel the similarities warrant more discussion. The Graesser paper is dismissed as irrelevant beacuse it "assigns agents into hard clusters of communities"  but this is a bit unclear to me - from what I recall Graesser et al. also explore communicative graphs with loosely-defined groups where there are more in-links than out-links.

## Missing references

- Tieleman et al. Shaping representations through communication: community size effect in artificial learning systems. NeurIPS ViGIL 2019
- Raviv et al. Larger communities create more systematic languages. Proc Roy Soc B, 2019. (In general I'd like some comparisons to the human linguistic evolution literature).

## Minor

- I think "varying group sizes and connectivities" would be a helpful edit to the title, as sizes/connectivities is ambiguous - I first interpreted it as size/connectivities of the architectures of speaker/listener implementations
- How exactly are the models limited to only either perceiving color and/or shape? I feel like I missed an actual discussion of this in the paper?
- L124 instruction is not a pixel-based object, I assume?
- L172 How is the VAE trained?
- L325 "...evidences the benefit of emergent communication involving larger number of participants rather than concerns." unclear sentence - what does concerns mean here? Do you mean it's better to have larger group sizes than smaller group sizes?
- [David, 1969] citation should be [Lewis, 1969]
limitations_and_societal_impact: Yes
review_title: null

Meta-review: This paper studies the effect of population size and connectivity of the communication topology of those populations in emergent communication and referential games. While population size has received some (very limited) attention in the literature, how the topology of the communicating agents is organized has been quite understudied. The authors report both communicative accuracy as a function of populations/connectivity but also present analysis of the resulting languages.

Overall, this has been among the most productive authors-reviewers interaction I've experienced. Authors have worked with reviewers to clarify questions and address concerns. Some new content has been generated during the discussion period and so I would ask the authors to update their manuscript respectively to reflect these discussions.
Decision: Accept (Poster)