Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: In this work, the authors investigate the the ability of agents to engage in turn taking through a simple language based game. In this game, agents can emit information through a shared channel and using the information the agents needs to solve the task. The authors find that agents that develop turn taking achieve higher score

Contributions:
1. Exploration the turn taking strategies between agents through a language based game. 
2. Initial results indicate agents that do turn taking are able to solve tasks quicker than non-collaborative agents.
strength_and_weaknesses: Strength and Weakness:
1. The basis of this work is interesting and studying turn taking in collaborative tasks in an understudied area. However, this work seems very preliminary and exploratory work and needs more analysis to be performed.
2. Why do the agents take lesser turns with partial information in the symmetric game? Shouldn't  higher accuracy indicate more communication/exchange of informations between the agents in Table 1(b)?

clarity,_quality,_novelty_and_reproducibility: Exploring turn-taking for conversational agents through a language based game and exploring the different strategies to enable turn-taking aspects are novel in this work.
summary_of_the_review: In this work, the authors investigate the the ability of agents to engage in turn taking through a simple language based game.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The paper takes a look at emergent turn-taking in communication. To study this, the paper sets up a language game, which is a cooperative task, and information needs to be shared over a communication channel to be successful at the task. The authors introduce a condition that disrupts communication when turn-taking is violated. Through different task settings and reward engineering, the authors show how agents can be biased towards taking turns and how taking turns might improve the efficiency of learning a task.
strength_and_weaknesses: ### Strengths:

- **************************Novelty:************************** The paper is unique in its selection of its problem to understand the emergence of turn-taking.
- ******************Clarity:****************** The paper, the methods, the metrics were quite easy to read and understand.
- The paper provides a good framework for how a communication channel might behave when overlapping messages are sent.

### Weaknesses:

- My main concern with the paper and the conclusions it draws are weak because of the simplistic task setting. For instance, it is unclear how the work might extend to future directions that the authors mention: 1. understanding the origins of turn-taking in human communication and 2. developing conversational agents that can achieve turn-taking naturally.
- The paper's conclusions (agents struggle to agree on turn-based strategies and that turn-taking agents achieve higher scores) seem pretty narrow. The paper did not convince me that they extend beyond the simple language game proposed by the authors.
- I don’t think the speculative claims in section 6.3 of functional vs cultural transmission are sufficiently supported by the analysis.
clarity,_quality,_novelty_and_reproducibility: Detailed in the main review.
summary_of_the_review: The papers claims are not sufficiently supported by the tasks /agents tested. The tasks are too simplistic to improve our understanding of human communication or improving conversational agents.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The authors have designed a simple language game to develop turn-taking conventions for neural agents. The game is a co-operative game, where two agents get a partial view of an object and the objective of the game is to reconstruct the original object with the lowest number of turns through mutual communication. Communication is done through a single channel that prevents both agents to speak at the same time, thus encouraging them to take turns while communicating. The partial view is constructed by masking some aspects of the original object. Overlaps were mostly modeled as noise, but other cases like misunderstanding and walkie-talkie were also experimented with.

The LSTM architecture was used to generate embeddings of the partial views of the agents. REINFORCE was used as the policy gradient algorithm for setting rewards. As for metrics, accuracy is used to measure if the prediction of the masked features. Experimental results show that agents trained under standard training objective tend to lean towards the agent with more information speaking most of the time and agents are able to solve the game 92% times, whereas with reciprocal objective, it is 98%.
strength_and_weaknesses: 1. Created a simple and effective game to model the problem.

2. Showed experimental results for situations that both worked and did not work.

3. Could have used BERT instead of LSTM to generate embeddings.
clarity,_quality,_novelty_and_reproducibility: The work targets an interesting aspect of communication and provides a really nice experiment to explore the problem, which has give scope for future work in the field. The experimental problem formulation is novel and have been explained properly in the paper. More implementation details would help in the reproducible aspect.
summary_of_the_review: 1. It would be nice to see how the experiments perform if BERT is used to get embeddings of the agents. BERT is one of the state of the art models in NLP for generating embeddings.

2. Add more bottom padding to figure 3, as it hampers in reading.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The authors introduce a setting and game by which they study the emergence of conversational strategies when eliciting and discovering information. In particular, the authors show that commonly observed (among humans and animals) turn-taking behavior does not automatically arise in agents trained on the game, but that it does result in higher performance and that turn-taking is more easily adaptable and discoverable compared to other optimal strategies.
strength_and_weaknesses: Strengths:
- The authors clearly establish the experimental setting and the possible methods of modeling overlap/interference are well justified and detailed (3.1.2).
- Interesting discussion on the difficulty of learning the optimal strategy in a variation of the conversational game and a demonstration that turn-taking is easier to adapt to

Weaknesses:
- A small comment: in the Misunderstanding setting, using garbled random symbols seems unnecessarily punitive, as misunderstandings seem often rooted in truth and at least snatches of what is meant to be communicated.
- It would be nice to see a comparison of how the studied effects may vary depending on vocabulary size (proxy for how complicated the language or modality of communication is) and model size
clarity,_quality,_novelty_and_reproducibility: Clarity: The paper flows well and is well-motivated.

Quality: The experiments and analysis seem of high quality.

Novelty: The approach (game) is based on similar games for masked information discovery but its application in this setting to investigate turn-based strategies seems novel.

Reproducibility: Code is stated to be released upon deanonymization.
summary_of_the_review: This is an interesting paper that investigates how turn-taking can evolve as a means of conversational strategy. The setting is clear and the authors investigate several paradigms of interference/penalties that map to realistic conversational situations. The results are interesting and contradict some explanations for the rise of turn-taking, and merit further study.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper investigates the ability of AI agents to engage in turn taking through a language based game. This is an under-explored area and the paper's design of experiments and analysis is solid and inspire future work in this area. I am in favour of accepting the paper. 
justification_for_why_not_higher_score: NA
justification_for_why_not_lower_score: Good paper
Decision: Accept: poster