Emergent Communication with Conversational Repair
Authors: Mitja Nikolaus
Venue: ICLR 2024 poster (2024)
Keywords: emergent communication
conversation
signaling games
language evolution
Primary area: applications to neuroscience & cognitive science

Abstract: Research on conversation has put emphasis on the importance of a multi-level communication system, in which the interlocutors aim to establish and maintain common ground. In natural conversations, repair mechanisms such as clarification requests are frequently used to improve mutual understanding.
Here we explore the effects of conversational repair on languages emerging in signaling games. We extend the basic Lewis signaling game setup with a feedback channel that allows for the transmission of messages backwards from the receiver to the sender. Further, we add noise to the communication channel so that repair mechanisms become necessary for optimal performance.

We find that languages emerging in setups with feedback channel are less compositional.
However, the models still achieve a substantially higher generalization performance in conditions with noise, putting to question the role of compositionality for generalization.
These findings generalize also to a more realistic case involving a guessing game with naturalistic images.

More broadly speaking, this study provides an important step towards the creation of signaling games that more closely resemble the conditions under which human languages emerged.

Published as a conference paper at ICLR 2024
EMERGENT COMMUNICATION TIONAL REPAIR
WITH CONVERSA-
Mitja Nikolaus CerCo, CNRS mi wwwa.iikklusssee. fr
ABSTRACT
Research on conversation has put emphasis on the importance of a multi-level communication system, in which the interlocutors aim to establish and maintain common ground. In natural conversations, repair mechanisms such as clarification requests frequently used to improve mutual understanding. Here explore the effects of conversational repair on languages emerging in signaling games. We extend the basic Lewis signaling game setup with a feedback channel that allows for the transmission of messages backwards from the receiver to the sender. Further, we add noise to the communication channel so that repair mechanisms become necessary for optimal performance.
We find that languages emerging setups with feedback channel are less com- positional. However, the models still achieve a substantially higher generalization performance in conditions with noise, putting question the role of composition- ality for generalization. These findings generalize also to more realistic case involving guessing game with naturalistic images.
More broadly speaking, this study provides an important step towards the creation of signaling games that more closely resemble the conditions under which human languages emerged.
INTRODUCTION
Conversation analysis has been describing human conversation interactions between speaker and listener, in which the interlocutors are using multiple communicative levels to negotiate mutual understanding (Scheglotf et al. 1977 Schegloff 1982 Clark & Schaefer 1989 Clark 1996 Pick- ering& & G
Whenever speakers are verbalizing their communicative intent to a listener, thereby communicating some information, listeners can either acknowledge (explicitly or implicitly) the receipt of this information or initiate repair routine (e.g., ask for clarification in case they did not understand the speaker correctly).
While conversational repair mechanisms such as clarification requests (also known as other-initiated repairs) have been found to present in a large range human languages (Tabensky) 2001: Dinge- manse & Enfield 2015), most recent research on language evolution has focused on unidirectional communication channels, thus only allowing information flow from the sender to the receiver, and not backwards. However, for basic other-initiated repair emerge, a feedback information flow from the receiver to the sender is necessary.
In this work, study the role of conversational repair for the nature of languages emerging in signaling games (Lewis) 1969). We extend widely-used basic signaling game setup to allow for the flow of feedback messages from the receiver to the sender, thus implementing bidirectional model of communication.
By studying the languages emerging in this setup, we find that they generalize better to unseen test examples under noisy conditions, while showing a substantially lower degree of compositionality as measured by topographic similarity. We validate this result for range of different noise levels, messages lengths, and input space sizes.
Work performed Aix-Marseille University.

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Finally, we develop more realistic guessing game setup with naturalistic scenes based on the Guess- What?! dataset (De Vries et al. 2017). in which the receiver needs to discriminate a target object from set of distractor objects within the same visual scene. Our findings regarding the improved performance under noisy conditions generalize to this more realistic setup.
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RELATED WORK
2.1 COMPUTATIONAL MODELING OF EMERGENT COMMUNICATION
Computational models of emergent communication aim implement aspects of human language evolution using communication games. While early attempts used Bayesian modeling to study the emergence of syntax using the so-called iterated learning model (Kirby & Hurford 2002; Kirby al. 2007), more recent approaches are leveraging deep reinforcement learning approaches to scale
models up to more realistic learning scenarios (Lazaridou et al., 2017 Lazaridou Baroni
2020 Guo et al.] 2022, Chaabouni et al. 2020 Lazaridou et al.] 2018 Chaabouni et al.) 2022
Rodríguez Luma et al. 2020).
In many studies, emergent communication is studied in basic Lewis signaling game (Lewis) 1969), which involves a sender and a receiver. The sender is required to communicate some information to the receiver through communication channel with limited capacity. Most models only consider a unidirectional communication channel, without any possibility for information flow backwards from the receiver the sender, therefore not allowing for any conversational repair mechanisms
to emerge. Exceptions are the game setups in Evtimova et al. (2018); CCo et al. (2018); Graesser al. (020),, which allow for multi-direetional flow of information. However, these studies did not consider communication channels with noise and consequently there exists no pressure for repair mechanisms emerge. Jorge al. (2016) analyzes languages emerging in a bidirectional signaling game with noise, but the noise is added to the communication channel in a way that it is not directly detectable by the message receiver.
Here we focus on a bidirectional communication game setup, in which sender messages are replaced by special noise token with certain probability. Thereby, the receiver can in principle learn to detect the presence of the noise token and initiate conversational repair routine.
Compositionality and Generalization A range of computational studies has explored composi- tionality and generalization in emerging languages. Chaabouni et al. (2020) studies the phenomena in principled approach and found that agents succeed to communicate and generalize even to unseen objects without the emerged languages necessarily being compositional according to a range of measures. The authors find that generalization capabilities emerge if the input space is large enough. Rita et al.] (2022a) looks into multi-agent game setups and finds that sufficiently hetero- geneous populations produce more compositional languages with an increasing number of agents. These results are in line with research on experimental studies with human subjects (c.g., RRaiiy al. 2019). Rita al. (2022b) shows that the commonly used loss can be broken down into an information term and co-adaptation term, and that controlling for overfitting on the co-adaptation loss increases compositionality and generalization performance. Other studies explore the role of template transfer (Korbak et al. 2021), communication channel capacity (Gupta et al. 2020), or communication over sets of objects (Mu & Goodman 202T).
In our work we directly compare the generalization performance and compositionality of models with unidirectional communication channel to those with an additional feedback channel.
2.2 CONVERSATIONAL REPAIR IN LANGUAGE EVOLUTION
Historically, a large portion of research in linguistics has been dedicated to find universals in the syntax of human languages. While the existence of such a Universal Grammar is disputed, more recent trends highlight the possibility to describe universals with respect to the use rather than the structure of language. For example, it has been argued that certain communicative feedback devices such other-initiated repair could be universally present in human languages (Dingemanse et al.) 2013 2013), Dingemanse & Enfield 2015). Such universals of conversation are not explained by innateness, but rather by selective pressure towards the evolution of common optimised forms that
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is exerted by the conversational environments (Dingemanse al. 2013 Roberts & Mills 2016). As
such mechanisms form major building blocks of human communication. is important to investigate how they impact the emergence of structure in language (Silva & Roberts, 2016). Healey et al. (2007) analyzes languages emerging between human interlocutors in a graphical language game and finds that repair is key for the emergence of complex symbol systems. [Mills & Redeker (2022)
suggests that self-repair increases the abstraction of emerging message systems.
Lemon 2022)
sketches out framework for emergent communication with conversational ground- ing. Agents should be able to detect disagreements and resolve them, in order to maintain a common ground. Targeted feedback signals facilitate the coordination between communication partners. Re- lated computational implementations can be found for example in [Steels (1995), where model for vocabulary formation within conversation that includes simple feedback mechanisms for responses and message aeknowledgement is proposed. Other examples include (Tria et al. (2012), which fo- cuses on "blending repair", strategy that exploits the structure of the world to create new words, as well as de Ruiter & Cummins (2012), proposing bayesian model of communication in which repair sequences are initiated if the entropy of the prior and posterior probability distributions over possible intentions surpass certain threshold. Finally, van Arkel et al. (2220) compares pragmatic reasoning and other- other- initiated repair, using bayesian modeling and complexity analysis.
In our work, we explicitly study the role of conversational repair by directly comparing models with and without feedback channel regarding the generalization performance and the compositionality of the emerging languages. Crucially, we leverage deep-learning based models that scale more realistic input, instead of only small-scale toy language game setups.
3 METHODS
3.1 BASIC SIGNALING GAME
following common practices in the literature (Kottur) cet al. 2017 Lazaridou et al., 2018 Chaabouni et al. 2020; Rita et al. 2022b). In the following, we will describe the details for the baseline used in all experiments.
We implement signaling game Lewis 1969)
Two agents communicate using symbols in a discrimination game. A sender agent S is provided with an input object og, sends a message token m E X using discrete symbols the receiver agent R. The vocabulary of possible tokens is denoted as X. The receiver needs to discriminate the target object from a set of distractor objects O by using the information provided in the message M. The input objects are defined by number of attributes A each with possible values V. An object is encoded using concatenation of one-hot encodings for each attribute, i.e. the input dimensionality is |A| [V]. The capacity of the communication channel is defined by the number of symbols in the vocabulary |X| and the message length [M
Both sender and receiver implemented as gated Recurrent Neural Networks (RNNs) using single- layer GRUs with layer normalization (Ba et al. 2016). In the basic setup, the number of distractor objects (including the target) |O] is set to 2. The parameters On of the receiver are optimized using a cross-entropy loss:
Lreceiver (fn) (00g(To,,00000,M1)
where n is the current policy of the receiver.
In parallel to the receiver, the sender agent is trained using REINFORCE Williams
1992):
IM
Loender = Σ r. log(TTe (mg (m, m;-1)) 1=0
(2)
where Tas is the current policy of the sender, m; is the message token at time step t, and is the reward (r = 1 if the receiver chooses the correct object from the set of distractor objects and r = otherwise). We further use running mean baseline to reduce the variance of the gradients well as entropy regularization to encourage exploration. At training time, the messages from the speaker sampled from the current policy, at test time we employ greedy decoding.
We split the set of all possible objects into a training set (90%) and a test set (10%). Further hyper- parameters and implementation details can be found in Appendix A.T The source code of the mod-
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els and all experiments is publicly available at hatpp::/gg..uu...ommmttttaaaaaaa emergent emergentt communication
3.2 BASIC SIGNALING GAME WITH NOISE AND FEEDBACK
To explore the effects of feedback, make adjustments to the baseline model described in the preceding section. First, we introduce noise to the communication channel: With a probability of Proise: each token in the message M is replaced with special noise token M' denotes the mes- sage after manipulation with the noise. Secondly, we allow the receiver RNN to generate feedback messages. At each timestep, the receiver RNN consumes the sender message token and produces feedback token n E Y. The sender RNN consumes this feedback token in addition to its last turn's output (both tokens are embedded and afterwards concatenated).
The loss functions for the agents with feedback are as follows:
Lpeceiver (b -log( (o, M,
(3)
[M Σ t=0
Isender (f6((s) =
log Tag (melos, my 1,71,7
(4)
We set |Y| to 2, i.c. the receiver only produces binary feedback. This allows a receiver agent to use the feedback channel for example to send acknowledgements open clarification requests Dingemanse & Enfield 2015). We leave the study of larger feedback channels for future work. The architecture of the model with feedback channel is displayed in Figure
Target object
Sender
RNN
RNN
RNN
RNN
Candidate objects
Receiver
RNN
RNN
RNN
RNN
MLP
Softmax
MLP
3R
Figure I: Architecture of signaling game with feedback channel. Both the Sender RNN (RNNs) and Receiver RNN RRNNR) are unrolled in time.
3.3 GUESSWILAT SIGNALING GAME
In order to test whether the results observed on the toy signaling game setup generalize to more realistic game setups, we develop another game setup in which agents communicate about objects in naturalistic images. In this game, the receiver has to discriminate a target object from a set of distractor objects that all present in the same visual scene. This task resembles common communicative task, in which speaker is trying to refer to single object within visual scene !
The proposed game based on the What?! dataset (De Vries al. 2017). which was initially designed to create models of grounded task-oriented dialog. Here, we only use the annotated image data, which consists images annotated with objects and their corresponding bounding boxes (Lin) et al. 2014). For each image, we select one of the objects as the input object and use the remaining objects as distractor objects The remaining task procedure as well as the model implementation
'See Section 4. 4.1.4 for discussion alternative noise implementations.
"Related work has proposed to study emergent communication using images from ImageNet (Russakovsky al. 2015). Here, propose task which relies discriminating objects within the same visual scene as opposed to different images, which is arguably harder and at the same time close communication prohlems that humans are usually facing: Referring to an object the shared visual environment.
We constrain the maximum number of distractor objects to 10. If there are more objects available, randomly sample subset of 10 objects.
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identical to the basic signaling game (ef. Section 3.1 Two example images are shown in Appendix A.2
Following the procedure described in De Vries et al. (001)), select all objects with bounding boxes a minimal size (area 2 500pzz). We further discard all images that contain only one object. For each object, extract features from the corresponding bounding box using Vision Transformer (vit-b-16: Dosovitskiy et al., 2020). which yields 768 dimensional vectors. We keep the original train and validation splits as defined in CoCo (Lin al. 2014). In total, there are 70,702 images (385,961 objects; 5.5 per image on average) in the training split and 8,460 (45,541 objects; 5.4 per image on average) in the validation split (which we use as test set).
3.4 EVALUATION
For each setting, we start 3 different runs with varying random seed and report the mean and 95% confidence intervals for metrics unless stated otherwise. We evaluate the models by measuring accuracy held-out test split (test_acc). We further report test accuracy in separate forward pass for which the channel noise is disabled (test acc no noise). This allows us to investigate how models performing under optimal conditions even if they we measure the compositionality of the emerged languages using topographic similarity (topsim; common practice in the language emergence literature (Lazaridou
trained with exposure noise. Finally,
Brighton & Kirby, (2006), et al. [2018] Chaabouni et al. 2020 Li & Bowling 2019). For fair comparison, the compositionality metric calculated in the separate forward pass during which the channel noise is disabled.
RESULTS
4.1 BASIC SIGNALING GAME
4.1.1 EFFECT OF NOISE
We start by investigating the case of (VI), = (4, 4) for increasing amount of noise: Proise {0. 0.1,0.10... 0.3, 0.5, 0.7, 0.9}. To ensure convergence of the agents, following the results of ChCaabouui al. (2020), employ them with large enough channel capacity: A vocabulary size X of 2 and a message length |M| of 10
lest_acc aCc
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Figure 2: Generalization performance and compositionality scores for models as a function of chan- nel noise Proise.
As first sanity check, observe that without noise, both models perform optimally (testLace z 1). When comparing the test accuracy in settings with noise, we observe that for all settings the models with feedback outperform the baseline models. This suggests that the feedback channel allows the models to repair the communication under noisy conditions. Additionally, we find that higher noise increases the performance advantage of feedback channel up noise level of Pnoise 0.7. At
"In the case of (I, IVI) = (4,4) the input space is |V||A| 4 256. In that way the channel capacity
sufficiently larger than the input space: |X| |M| 2D 1024
256.
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Proise 0.9 the advantage decreases again and the model convergence becomes more unstable (as indicated by the increased variability of performance between runs).
Under optimal conditions, if the channel noise is removed, both models perform approximately par, suggesting that while the feedback models can repair communication under noise, this does not harm their performance when noise is absent.
While the test accuracy feedback models under noise is clearly superior, we observe a substantial drop in the topsim score for these models. This suggests that while the feedback allows the models to increase test accuracy in conditions with noise, this is coinciding with an decrease in composi- tionality (as measured by the topsim score). While Chaabouni al. (2020) already observed that compositionality is not necessary to achieve generalization, here we even observe an opposing trend.
Analysis of Feedback Messages In order to gain a better understanding of how the models employ the feedback channel to repair the communication, we analyze the messages of converged model for the case Pnoise 0.5
To this end, we record the messages sent by the sender well the feedback messages by the receiver for the test set. Then we calculate the correlation (Matthew's Correlation Coefficient; Matthews 1975) of receiver messages with (1) the presence of noise in the sender messages, (2) the sender messages (excluding messages that contain noise), well as (3) the one-hot encodings of the two input objects. Figure 3 visualizes the correlations using heatmaps.
receiver mesrage 0
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3 4 sender sender_messape messame.ssgge....o.o www.ssoge____naaa... ns_nels
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Input_object_0
Input_object,
Figure 3: Matthew's
Correlation Coefficient between receiver messages and the presence of noise, the sender messages, and the one-hot encodings the two input objects. The messages are recorded while the agents are playing the signaling game on the test set.
When observing the response patterns we find that the feedback message tokens do not depend on the presence of noise token in the previous leftmost graph). This indicates that the feedback tokens are not used as open clarification requests, i.c. they are simply signaling the presence of noise back the sender.
(all correlation coefficients close to 0 in the
The second graph shows that there is however positive correlation between the sender messages and receiver messages in the subsequent turn. Following 1 sent by the sender, the receiver usually responds with and vice versa. In this way, the feedback messages can function as an acknowl- edgement, signaling the received message back the sender. For later messages (after message approximately), we find negative correlation that is slightly delayed.
Finally, we find that there are also substantial correlations between the properties of the candidate objects (target and distractor) and the receiver messages. This hints that the feedback messages also serve to communicate certain aspects the candidate objects to the sender (who does not have access to both objects). In this way, sender and receiver can be co-constructing meaning during the course of the interaction.
Understanding the exact mechanisms of the feedback messages remains challenging, the models could create any arbitrary messaging code. Still, we would like to estimate to which degree the models actually develop an efficient code to solve the signaling game. We implement an additional setup in which the receiver model is encouraged (using an additional loss term) to only signal the
We also analyze the messages of 2 other with different seeds and observe highly similar patterns.

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presence of noise back to the listener. The details of this setup well result graphs can be found in Appendix A.3 We find that while this case the receivers indeed signal the presence of noise, the generalization performance lacks behind that of models who develop their own feedback messaging code (but is still better than baseline performance without any feedback). The best performing mod- els leverage the feedback message channel to exchange information more efficiently than models using the feedback channel for simple open clarification requests.
4.1.2 EFFECT OF INPUT SPACE
To ensure that the observed effects not only phenomenon of the specific input space, periment with multiple other configurations larger and smaller input spaces. We keep the noise ratio at Phoise
0.5 and vary the number of input attributes |A| and values [V]: (IA), {(2, 10), (4, 4), (3, 10), (2, 100), (2, 1000), (10, 1000)}.
The results depicted in Figure We find that for all tested configurations, the feedback channel alleviates the detrimental effects of noise. The largest effects are observed for very small input sizes (10, 1000). Notably, the input space is even surpassing the channel capacity in the three larger input space settings. In line with the findings of the previous section, we also observe decrease topsim scores for most settings. Also, the models' generalization performances are comparable if the channel noise is removed.
(14), [V] (2, 10) or very large ones (|A],,
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Figure 4: Results for different input space dimensions.
4.1.3 EFFECT OF MESSAGE LENGTH
Another important hyperparameter of the game setup is the message length of the communication channel. Here, we investigate the influence of this parameter on the performance advantage of feedback channel.
acc
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Figure 5: Results function of message length |M].

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We set Pnoise 0.7 and vary the message length: [M] {1,3,5, 5, 10, 20, 30, 50}. As shown in Figure 5 we find that starting from [M] = 5. performance advantage for the models with feedback emerges. The advantage increases until length of 30, afterwards the gap between the performance of two model types decreases again. With a sufficiently high message length, the sender can simply repeat each message multiple times to increase chances of successful transmission without the need for any receiver feedback. When comparing the conditions |M] 10 and [M] = 20, we find that models with an additional feedback channel and [M| = 10 even outperform models with unidirectional message channel that is double in size ([M) 20). This suggests that in this configuration more efficient allow models for feedback communication than increase the capacity of the unidirectional message channel.
4.1.4 EFFECT OF NOISE IMPLEMENTATION
In our basic game setup the noise is implemented using a special token and therefore simply detectable by the receiver agent. This relates phenomena such listener not understanding syllable or word because of some increased background noise. In order to model other phenomena, such misunderstandings, the noise on the channel can be implemented of the message token with another token from the vocabulary. In this case, the presence of noise is directly detectable by the listener and therefore more negotiation might be necessary in order to obtain common ground. We therefore expect lower generalization performance with this implementation of noise.
random permutation
We run the experiments described in Section 4.1.1 with this alternative implementation of noise. The results are shown in Appendix AA.4 find that for this kind noise, the generalization performance drops more substantially with increasing noise level (e.g. mean testLacc of 0.70 vs. 0.89 for Pnoise 0.7), validating our hypothesis that this kind of noise more challenging for communication. However, still observe that feedback partially alleviates the effects of noise: The models with feedback outperform the baseline models. The compositionality of languages as measured by topsim is again lower for the models with feedback.
4.2 GUESS WHAT SIGNALING GAME
Based on the Guess What signaling game described in Section 3.3 we perform set of experiments investigate whether the findings on the basic signaling game also hold on more realistic commu- nication game setups with naturalistic images.
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Figure 6: Generalization performance for models in the GuessWhal signaling game as function of channel noise Pnoise (left) and message length |M (right).
We initially keep the same channel capacity in the basic signaling game setup, vocabulary size [X| and a message length [M] of 10. The left plot in Figure shows the effect of increasing noise models with and without feedback channel. In line with the previous findings, find that the feedback channel alleviates the effects of noise, with a peak in performance difference that is again around Proise = 0.7.
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Regarding the role of message length, the right plot in Figure 6 shows that the performance ad- vantage increases with increasing [M] (with a fixed channel noise of Pnosse = 0.5). In contrast to the findings the basic signaling game, this advantage does not decrease for the largest message length (M) = 50). When evaluating the generalization capabilities without noise, both model types perform comparably (see Appendix A.5
DISCUSSION AND CONCLUSION
The findings of this work suggest that in signaling games with noisy conditions, superior per- formance can be achieved when models are allowed to send feedback messages backwards from the receiver to the sender. While this increases the generalization performance of the models, the compositionality of the emerged languages decreases.
This drop in compositionality might be explained by multiple factors. First, as already shown in Chaabouni et al. (2020). there is not always a direct link between compositionality and generaliza- tion performance. Secondly, natural languages are not perfectly compositional either, in many cases meaning is dependent on context (Goldberg 2015). When allowing for bidirectional information flow between sender and receiver, is possible that both agents are jointly co-constructing mutual understanding and thereby creating contextualized meanings. Consequently. the sender messages become less compositional and more context- -deeexttde (see also Section 1.1 Recently, Korbak ct al. (2020); Conklin & Smith (2023) also highlighted the limitations of topsim as measure of compositionality in emergent communication, to which our results add additional evidence
Lemon (2022) pointed to lack of rision-and-languaal datasets that explicitly require conversational
grounding in additional to symbol (visual) grounding. In this work we designed a simple referential signaling game that allows for the study of conversational repair in the context of a referential game within naturalistic scenes. In line with the findings from the basic signaling game, we find that a feedback channel allows models to improve their generalization performance under noise. With the development of models for an efficient generation of clarifying questions in dialog being an open challenge (Kiseleva al., 2022). the proposed setup allows for the study of the emergence of crucial mechanisms for successful dialog, such as basic communicative grounding acts (Clark & Schaefer 1989 Clark 1996).
So far, this work only investigated setups with binary message and feedback channels. To study the emergence of more advanced repair mechanisms such as restricted requests or restricted offers as opposed to open clarification requests (Dingemanse & Enfield 2015), the capacity of the message channel should be increased in subsequent works.
We experimented with two alternative implementations of noise (ef. Section 4.1.4), but even fur- ther setups should be investigated in the future and might trigger the emergence more advanced repair mechanisms. This includes for example combining the two proposed noise implementations (special noise token for modeling non-underst and token permutations for modeling misun- derstanding) within single model, well as non-uniform distributions of noise. Relatedly, currently do not add any noise on the feedback messages from the receiver. While this design choice was taken to study the emergence of basic conversational repair, it is not realistic and will need to be adapted in the future to perform more extensive experiments on nested clarification requests van de Braak al. 202I). Other axes of future work could extend the model explore the emer- gence of preference for self-repair over other-initiated repair, which is typically found in human conversation (Schegloff et al. 1977).
As indicated from these numerous opportunities for future work, the current work contributes an- other important step to the ongoing efforts on closing the gap between signaling games and realistic models of language evolution (Chaabouni et al. 2019 Rita et al.) 2020) Galke et al. (022).
Kottur et al. 2017) also observe that agents exploit bidirectional communication channels to create non- compositional languages. They counteract by limiting the vocabulary size and removing one agent's 's memory every timestep. which prevents messages from being context-dependent
LaCroix (2019) questions compositionality as a target for language evolution research more generally. The author argues that focus should instead be put on reflexivity, as it is more consistent with gradualist approach to language origins. Future work is required to operationalize measures of reflexivity and apply them computational emergent communication experiments.

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ACKNOWLEDGMENTS
Many thanks Lukas Galke for fruitful and in-depth discussions related this work. Further, this work was substantially improved thanks to the reviewers who provided very constructive feedback.
This work, carried out within the Labex BLRI (ANR- SANR-1ILAN 1ILABX-0036) and the Institut Convergence ILCB
has benefited from support from the French government, managed by the French National Agency for Research (ANR) and the Excellence Initiative of Aix-Marseille University (A MA*MID.X).
The project leading to this publication has received funding from Excellence Initiative of Aix- Marseille A*MIDEX (Archimedes Institute AMX-19-IET-009), French "Investissements d'Avenir" Programme.
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A APPENDIX
A.I HYPERPARAMETERS
Hyperparameters were configured as indicated Table unless stated otherwise.
optimizer
Adam
initial learning rate
0.001
batch size
1000
gradient_clipping
message_enggle
10
vocab_sia size
2
sender eeneeddinmb size
16
sender whiddhhddddlll .dim
128
sender sender_entrope ennropy coefficient
0.01
receiver hetiivereebbddiingg.... size
16
receiver_hidden.co _dim
128
httpreeenntoooyooffffiiiiee 0.01
Table 1: Hyperparameter settings.
A.2 GUESSWHAT SIGNALING GAME EXAMPLES
Figure shows two examples for the images used the Guess signaling game described in Section 3.3 The receiver agent needs to discriminate the target object (for example the gray parrot the left figure) from the other objects in scene (the two other parrots). The task becomes challenging for cases in which the target object is highly similar some of the distractor objects (for example, discriminating on of the sheep from the others in the right image).
Figure 7: Examples for images used in the GuessWhat signaling game. Candidate objects are high- lighted with the colored bounding boxes.
A.3 RESULTS WITH ADDITIONAL LOSS TERM
We train models with an additional loss term the receiver side, that is encouraging the receiver messages to signal the presence of noise the sender messages. The loss defined as cross-entropy between the receiver message token logits and the presence of noise in the preceding sender message token (1 noise is present and 0 otherwise). The performance of this model is displayed in Figure
Additionally, we plot analysis of the feedback messages (see also Section (.1.1) for model with
Pnoise = 0.5 in Figure [ This shows clearly that the model is signaling the presence of noise, and (almost) other information.
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acc
topsim
1.00
1,00
0.50
0.95
0.40
0.90
0.85
0.80
0.85
0.30
0.25
baseline
0.80
0.70
0.20
feedback Laddiioonaalaaaa Joss
0.75
0.15
0.1
0.3
0.5
0.7
0.9
0.1
0.3
0.5
0.7
0.9
0.1
0.3
0.5
0.7
0.9
noise
noise
noise
Figure& 8: Generalization performance and compositionality scores for models as function of chan- nel noise Proises including model with additional loss term.
0.75
receiver message
0.50
0.00
-0.25
-0.75
10
4
sender messaeesseeee.. is_nolse
sender message
inpu_object_0
Input_object_1
Figure 9: Matthew's Correlation Coefficient between receiver messages and the presence of noise, the sender messages, and the one-hot encodings of the two input objects.
A.4 RESULTS WITH ALTERNATIVE NOISE IMPLEMENTATION
Figure I0 presents the effect the alternative noise implementation using message token permuta- tion instead of a special noise token. Figure presents analysis of the feedback messages for this setup.
test acc
test acc. no. noise
topsim
1.00
0.45
0.95
0.40
0.35
0.80
0.25
0.6
baseline feedback
0.75
0.20
0.1
0.3
0.5
0.7
0.9
0.1
0.3
0.5
0.7
0.9
0.1
0.3
0.5
0.7
0.9
noise
noise
noise
Figure 10: Generalization performance and compositionality scores for models function of alternative channel noise Pnoise-
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0.75
receiver nesssae
0.50
0.00
-0.25
5
-0.75
-1.00
sender message.eeee.. nonee
sender message
Input_object
input_object_
Figure 11: Matthew's Correlation Coefficient between receiver messages and the presence of noise, the sender messages, and the one-hot encodings of the two input objects for model with alternative channel noise of Pnoise 0.5. The correlation with the presence of noise is always 0, as there is no explicit noise token this setup.
A.5 ADDITIONAL RESULTS FOR GUESSWIIAT SIGNALING GAME
Figure noise.
12
presents the results on the GuessWhat signaling game when evaluated without channel
test acc no noise
test acc no noise
feedback
0.80
0.75
0.7
0.6
0.5
0.60
0
0.1
0.3
0.5
0.7
0.9
5
10
20
30
50
noise
messa geessage_length length
Figure 12: Generalization performance without noise for models in the GuessWhar signaling game function of channel noise during training Pnoise (left) and message length [M] (right).
17


Reviews:
[{"review_id": "ZX3Wn3Go1U", "reviewer": "Reviewer_R7xc", "summary": "The paper explores the effects of conversational repair mechanisms on emergent communication in signaling games. The basic Lewis signaling game setup is extended to allow bidirectional communication through an interleaved feedback channel from receiver to sender. Noise is added to the communication channel by replacing tokens at random. Models trained with a feedback channel are found to achieve higher generalization performance under noisy conditions, even though the resulting languages are less compositional.", "questions": "* Typo: “defined by the number of symbols in the vocabulary V” — the vocabulary was previously denoted by X; V was defined was the set of possible values the attributes may take.", "limitations": "", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The experiments are interesting, testing multiple game configurations and noise levels. The code is open-sourced. Results are robust across settings and replicated in two different game paradigms. The paper clearly explains the methods, results, and implications. Allowing conversational repair is clearly an important step towards better models of human language evolution.", "weaknesses": "Methods: The most significant concern is that the apparent lack of compositionality (arguably the 'headline' result) is an artifact of the specific choice of how to inject noise (via random i.i.d. replacement with a special token). Modeling *misunderstanding*, uncertainty over *meaning* or *interpretation* at the message level, rather than uncertainty over the literal content of each message token, would better reflect the cases where repair arises in real-world communication. \n\nOriginality: While some details of the specific implementation here is novel, a number of prior works have also introduced interactive repair mechanisms into signaling games and are not discussed. Even the classic Steels (1995) simulations included a form of (binary) repair. Here are some salient examples from the more recent literature. \n\n- van Arkel, Woensdregt, Dingemanse, & Blokpoel. (2022). A simple repair mechanism can alleviate computational demands of pragmatic reasoning: simulations and complexity analysis. CoNLL.\n- Tria, Galantucci, & Loreto (2012). Naming a structured world: A cultural route to duality of patterning. Plos ONE. \n- de Ruiter & Cummins. (2012). A model of intentional communication: AIRBUS (Asymmetric Intention Recognition with Bayesian Updating of Signals). SemDial. \n- Silva & Roberts. (2016). Exploring the role of interaction in the emergence of linguistic structure. EVOLANG.\n- White, Poesia, Hawkins, Sadigh, & Goodman. (2022). Open-domain clarification question generation without question examples. EMNLP. \n\nAdditional weaknesses:\n\n* The feedback channel only allows binary signals. It's unclear how the results would be affected by a higher-dimensional space of feedback. \n* Noise is only added to sender messages, but realistically noise would affect receiver feedback too, possibly shrinking the gap between the feedback vs. no-feedback models.\n* No comparison to recent related work like van Arkel et al. is provided to situate the advances here.\n\nIn summary, while this work provides a good first investigation, the contributions are somewhat modest and incremental given prior exploration of feedback channels. It's difficult to know which aspects of the findings are general consequences of repair mechanisms, and which aspects are artifacts of specific choices about how noise is injected in this task setup. Addressing the above limitations would strengthen the novelty and significance of the study.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "G5sP2JcpdO", "reviewer": "Reviewer_p6xc", "summary": "This paper introduced a new Lweis signaling game setup with a noisy communication channel for emergent communication. Through experiments in logical and pixel-level input, they showed that setups with the receiver’s feedback can achieve better generalization performance in a noisy environment. By reporting the topographical similarity, it also points out that a better generation does not usually mean a higher compositionality.", "questions": "1. How does the game end? Will they communicate a fixed number of time steps?\n2. How does the receiver generate the feedback token, through another MLP layer besides the one used in the final target selection?\n3. The drops in the compositionality are interesting. Since the sender’s message now depends on both the symbolic input and the receiver’s feedback, the semantic spaces could be disturbed. Have you tried to let the sender and the receiver share the same vocabulary?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper considers a novel setup with multi-step communication with the receiver’s feedback. It changes the original unidirectional communication to bidirectional communication, which can better resemble realistic human communication scenarios.\n\n2. The experiments designed multiple evaluation groups to test how a feedback channel helps alleviate the effects of noise, influence generalization, and compostionality. The experiments introducing realistic image referential tasks with objects in the same visual background also look interesting. The performance contrast between compositionality and generalizability and the potential cause of the receiver’s feedback is worth further investigation.", "weaknesses": "The current experiments only show that communication with the receiver’s feedback will generalize better in a noisy environment. It would be more comprehensive to understand this feedback behavior with further analysis:\n\n    a. The semantics of the feedback token: for example, is it more related to interaction regulation (continue to talk, no need to talk) or the attribute of the objects (clarification on some attributes)? \n\n    b. The messages updated by the sender: based on the receiver’s feedback, how the sender’s messages vary across different time steps. \n\n    c. Through multiple iterations, will the receiver provide less feedback or less informative feedback while they gradually build their common ground?\n\n    d. Will the emerged languages with additional feedback make the sender’s messages of different objects more separatable?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7qW0ZGPwSC", "reviewer": "Reviewer_3F5Q", "summary": "This paper introduces a basic notion of communication failure and opportunity\nfor recovery into the standard emergent communication signalling game.\nCommunication failure occurs when one of the sender's tokens is replaced with\na special noise token, and the listener is provided with a binary feedback\nchannel that could be used to indicate the need for recovery.  The empirical\nresults show that the binary feedback channel provides an increase in\nperformance in the presence of such noise but is not necessary in the noiseless\ncases.  This effect is robust across different hyperparameters as well as with\nboth symbolic and image embedding-based observations.", "questions": "- Page 2:\n    - Section 2.2: [Travis LaCroix's paper](https://www.semanticscholar.org/paper/Biology-and-Compositionality%3A-Empirical-for-LaCroix/6422d5e83caec99487936035cfbb2b0d18f2a76d) on reflexivity could be relevant\n- Page 5:\n    - \"instable\" -> \"unstable\"\n    - It would be better to use 95% confidence intervals instead of raw standard error\n    - Discussion: \"jointly co-constructing\" seems pretty relevant to Kottur et al. (2017), maybe a sentence connecting the two works would be appropriate\n    - \"lack vision-and-language\" -> \"lack of vision-and-language\"?\n    - Would it be possible to give a sense of what the correlation between the\n      presence of the noise token and the feedback request from the receiver\n      is?  It's not necessary for all of the experiments, maybe just the\n      initial basic ones.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "The paper is coherent: it has a simple, well-defined scope that is explained\nwell, pursued with reasonable methods, and has empirical data with supports the\nmain contribution with appropriate ablations.  While the contributions are not\nextensive or revolutionary, I do not see this as a problem because the\ncontributions are sufficient and the quality of the paper as a whole makes\na good building block in the field's body of literature.", "weaknesses": "I do not see any major weaknesses in the paper as a whole.  Any specific areas\nthat could be improved upon are mentioned in \"Questions\".  What puts my rating\nat an 8 instead of 10 is primarily the modest significance of the\ncontributions.\n\nA minor weakness is that there is no robust/empirical explanation for the cause\nof the drop in topographic similarity in the feedback-enabled agents.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)