Information-Theoretic Underpinnings of Generalization and Translation in Emergent Communication
Authors: Mycal Tucker
Julie Shah
Roger P. Levy
Noga Zaslavsky
Venue: Submitted to ICLR 2023 (2023)
Keywords: Emergent Communication
Information Theory

Abstract: Traditional emergent communication (EC) methods often fail to generalize to novel settings or align with representations of natural language. While these limitations may at first appear unrelated, in this work, we show how controlling the Information Bottleneck (IB) tradeoff between complexity and informativeness (a principle thought to guide human languages) helps to address both of these problems in EC. Specifically, we build on VQ-VIB, a recently proposed method for training EC agents while controlling the IB tradeoff, in addition to maximizing agents' utility. We find that increasing informativeness, which is a task-agnostic measure of how well a listener can reconstruct a speaker's meaning, allows EC agents to better generalize to novel settings and more challenging tasks. At the same time, in translation experiments between EC and English, we find that increasing EC informativeness only improves team performance up to a certain threshold, corresponding to the English informativeness-complexity tradeoff. Jointly, our results indicate the importance of training EC systems while controlling the informativeness-complexity tradeoff to simultaneously support improved self-play performance and human-agent interaction.

INFORMATION-THEORETI UNDERPINNINGS OF GEN- ERALIZATION AND TRANSLATABILITY IN EMERGENT COMMUNICATION
Anonymous authors Paper under double-bling blind review
ABSTRACT
Traditional emergent communication (EC) methods often fail to generalize to novel settings align with representations of natural language. Here, we show how controlling the Information Bottleneck (IB) tradeoff between complexity and informativeness (a principle thought to guide human languages) helps address both of these problems in EC. Using VQ-VIB, a recent method for training agents while controlling the IB tradeoff, we find that: (1) increasing pressure for infor- mativeness, which encourages agents to develop a shared understanding beyond task-specific needs, leads better generalization novel inputs; (2) VQ-VIB agents develop an EC space that encodes some seman- tic similarities and facilitates open-domain communication, similar to word em- beddings in natural language; and (3) when translating between English and EC, greater complexity leads to improved performance of teams of simulated English speakers and trained VQ-VIB listeners, but only up to a threshold corresponding to the English complexity. These results indicate the importance of informational constraints for improving self-play performance and human-agent interaction.
more challenging tasks and
INTRODUCTION
We wish develop artificial agents that communicate in grounded settings, via communication that enables high task utility, generalizability to novel settings, and good human-agent cooperation. Emergent communication (EC) methods, wherein agents learn to communicate with each other an unsupervised manner by maximizing reward function, take step towards this vision by producing
agents that use grounded communication (Lowe et al. 2017 7 2020 Lazaridou & Baroni 2020). While numerous EC methods have succeeded in training agents to communicate with each other to solve a particular task, they still fall short of the vision of generalizable and human- caamanniteerrretbble communication. For example, agents trained to discriminate between two types of images will fail to discriminate between sixteen images (Chaabouni et al. 2021b), and messages often violate human expectations for meanings (Kottur al. (017))
In this work, we take steps towards addressing these limitations by building on the information- theoretic EC approach of Tucker al. (2022). This approach connects EC with the Information- Bottleneck (IB, Tishby et al. 1999h framework for semantic systems (Zaslavsky et al. 2018 Za- slavsky 2020), via the vector-quantized variational Information Bottleneck (VQ-VIB) neural archi- tecture (Tucker et al. 2022). Specifically, VQ- VQB agents are trained to optimize tradeoff between maximizing utility (how well they perform task), maximizing informativeness (how well listener can infer a speaker's meaning, independently of any downstream task), and minimizing commu- nicative complexity (roughly the number of bits allocated for communication). While previous EC methods typically focus task-specific utility maximization (Lowe et al. (017). there is broad em- pirical evidence suggesting that human languages are guided by the IB poermatieenss--copllssee tradeoff (Zaslavsky et al.) 2018: 2019 2021 2022 Mollica al. 2021). Therefore, hypothesize that taking into account informativeness could improve EC generalizability to novel settings while adjusting complexity could improve the translatability between EC and human languages.
Results from our experiments support this hypothesis. First, we show that encouraging informative- ness allows EC agents to generalize beyond their training distribution to handle more challenging

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tasks and out-of-distributio objects, with VQ- QQB achieving the best performance compared to al- ternative EC methods. Second, we propose a simple method for translating natural language word embeddings (e.g., GloVe, |eeniinton et al. 2014) into EC signals and use that to simulate human- agent communication in a cooperative object-discrinination task. We find that team performance for English speakers and trained ECC agents improves with the communicative complexity of the EC system, but only up to a certain threshold, which corresponds to the complexity of the English object naming system. Together, our findings suggest that training EC agents while controlling the informativeness. complexxty tradeoff, in addition to maximizing utility, may simultaneously support improved self pllyppaay performance as well as human- agent interaction.
2 RELATED WORK
Our work builds upon prior research in emergent communication (EC), wherein cooperative agents trained maximize reward function. For example, a speaker may observe "target" image that a listener must identify from a pool of candidate images, based only on the speaker's 's emitted communication. Researchers in EC often consider the effects of different neural network architec- tures, training losses, or environmental factors on learned communication (Kottur et al. 2017 Mu & Goodman 202T; Kucinski et al. 2021 Tucker et al. 2021). In this work, we are primarily y concerned with how coimaattio-thhorrtti properties of agents' communication allow them to generalize or align with human languages, although we also improve upon an existing neural architecture.
2.1 GENERALIZATION OF EMERGENT COMMUNICATION
Several works consider the ability of EC agents to generalize to different settings than those which they were trained. For example, Lazaridou et al. (2018) train agents with symbolic inputs and use novel combinations of such inputs to test generalization. Other researchers use similar train-test gaps to measure the compositionality and generalizability of communication, but such experiments are necessarily restricted to domains with symbolic structure (as those allow recombination of features) (Andreas 2019 Kuciński et al., 2021 Spilsbury & Ilin 2022). Conversely, Chaabouni al. 2202bb) tests agents' abilities to generalize in image-based reference games to 1) more distractor images or 2) inputs from distinct dataset of images. They find that using harder tasks at training time is important for improving test-time performance and that using population-based voting improves cross-domain transfer. We similarly interested in the ability of agents to generalize to harder tasks and novel inputs, but we focus on training only single team of agents; research in learning dynamics due to population effects or changes in environment is complementary to our work.
2.2 LINKS TO NATURAL LANGUAGE
Complementing research of EC in self-play (evaluating agents that were trained together), many re- searchers explore connections between EC and natural language. Some top-down methods combine pretrained language models with finetuning grounded environments, but such methods can suffer from "drift" wherein agents learn to ascribe new meanings to words (Lewis et al.) 2017] Lazaridou cct al. 2020 Jacob et al. 2021). Other researchers train agents in self-play and then seek to connect learned EC to human- iitmannniteerretabll concepts natural language (Andreas et al. 2017;; Kottur et al.) 2017). Tucker et al. (202T) take a middle ground by training agents to communicate via discrete rep- resentations in continuous space, which can be linked to natural word embeddings via supervision during training. We seek to uncover methods for best connecting EC and human communication via translation, with no supervision data when training the agents. Based informatiinn-hhoreete analysis of human naming systems, we investigate whether producing EC that matches humans' complexity and informativeness enables better translation.
BACKGROUND: INFORMATION EMERGENT COMMUNICATION
Our work builds on the information-theone framework of Zaslavsky et al. 2018) for semantic systems, and especially its extension to scalable emergent communication in artificial neural agents, proposed by Tucker et al. 2022). Below we review the theoretical and empirical foundations of this line of work.
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3.1 EFFICIENT COMPRESSION AND SEMANTIC SYSTEMS
Zaslavsky et al. 2018 argued that languages are shaped by the need of speakers and listeners to efficienlly compress meanings into words. They formulated this objective using the Information Bottleneck (IB, TTihby et al. 1999) principle, which can be interpreted as tradeoff between the in- formativeness and complexity of the lexicon, and can be derived from rate-distortion theory (Shan-) non 1959, Harremoës & Tishby 2007). In this framework, speaker is characterized as proba- bilistic encoder S(cmm) that, given an input meaning m ~ p(m), generates a communication signal, A listener is characterized as probabilistic decoder D(mlee, that reconstructs the speaker's meaning from c. Given such agents, complexity is computed as the mutual information between the speaker's meanings and signals, [(m; c), which is roughly the number of bits that need to encode m using c. Informativeness measures how well the listener's interpretation, m,, matches the speaker's intended meaning; intuitively, this corresponds to how well listener is able understand a speaker. The agents' meanings, m and r,, are defined as belief states, i.e., probability distributions over feature vector representing the agents' environment. Therefore, maximizing informativeness is equivalent to minimizing the expected Kullback-Leible (KL) divergence between the agents' belief states, E[DK e. [m] [m]]] Minimizing complexity yields very simple but uninformative com- munication systems, while maximizing informativeness may yield very complex systems. Optimal speakers and listeners therefore jointly balance the tradeoff' between these two objectives by mini- mizing /(m; e) 3E[Dxt [.r [m]]iii]], where 2 0 controls the tradeoff. This optimization problem equivalent to the IB principle.
This theoretical framework has gained broad empirical support across human languages and multiple semantic domains, including color (Zaslavsky et al. 2018 2022), artifacts (Zaslavsky et al. 2019). and personal pronouns (Zaslavsky et al., (021). Specifically, it found that the ways languages map meanings into words achieve near-optimal compression in the IB sense, suggesting that the same principle may also apply to communication among artificial agents (Zaslavsky et al. 2017
Eisape et al. 2020 Chaabouni et al. 2021a).
3.2
VARIATIONAL INFORMATION BOTTLENECK (VQ-VIB)
Directly solving the IB optimization problem for high- high-digeeneee dimensional inputs and neural agents is chal- lenging, and in many emergent communication settings intractable. Tucker et (2022) proposed vector-quantized variational Information Bottleneck (VQ-VIB), a deep learming method that com- bines notions from vector quantization variational autoencoders (VQ-VAF, Van Den Oord et al.] 2017) and variational Information Bottleneck (VIB). In addition, Tucker et al. (2022) integrated the standard utility-maximize maximization approach with IB and showed how to train EC agents by trading off utility, informativeness, and complexity.
To apply IB to EC, note that the communication setup from $3.1 can be applied to Lewis reference games (ewiis 2008), depicted in Figure [ Here, (the speaker's internal representation) is produced by passing a target input, Zt. through a pretrained VAE to model perceptual noise. Thus, in our case m d A decoder, D, seeks reconstruct given the speaker's communication, Using this reconstruction, a listener agent, I., attempts to identify x from a set (ai,..,, Ic} of (noisily observed) candidate inputs by selecting y C. VQ-VIB agents fit within this framework:
Informativeness
H4
Zi
N
x,*
VAE
S
+c
D
m
m"
Un
X1
Jo
Ci
Complexity
Utility
O
C
C
Cnl
Figure 1: Communication setup in which agents coordinate to identify target input (c.g., image), It, among set of candidates C = (f1....,, TC) (see main text for details).
Figure 2: Our modified VQ-VIB architecture combines n quantized vectors into single mes- sage, enabling ke unique messages.
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probabilistic speaker, S. maps from m to parameters of Gaussian distribution, f and o, in continuous latent space, R. A latent vector, 2, is sampled from ((u(m), (m)) and discretized by looking up the nearest element of (trainable) codebook of k quantization vectors Ç E R The final communication vector output by the speaker, c, is this nearest quantization vector. That is, the VQ-VIB encoder is defined by ((c PP(c argminglle <1
In this information-theoreer emergent communication (TTEC) framework, agents maximizes a com- bination of utility and informativeness while minimizing complexity, with Ay. Xr, and Xo control- ling the relative weight of each term. In practice, agents are often trained with variational bounds for these terms, as shown in Equation [ for VQ- agents.
maximize
E (m,, y)] 1/ m m1]
AeE ((L)))) 1)]l (I)
Utility, (z y). is task specific,,,,, and in our settings it measures the listener's accuracy in identifying the target input. Informativeness is task-agnostic, measuring the listener's ability to reconstruct the speaker's belief state (m) given a communication vector c. regardless of any downstream task. This term may be approximated by -llm ril1 Lastly, for VIB agents in partieular, the Gaussian sampling in the latent space permits the upper bound of complexity as the KL divergence between ((((m), (m)) and unit Gaussian (Alemi et al., 2017). Backpropagation through sampling is accomplished via the reparametrization trick (Kingma & Welling 2013), and a straight-through estimator for the discretization process is used, as VQ-VAE (an Den Oord et al. 2017).
4 TECHNICAL AAIIICALLPPPEE APPROACH
We extend beyond prior work in two ways. First, we modify the VQ-VIB architecture to increase the effective vocabulary size of agents. As we later validated in experiments, this architectural change allows agents to learn much richer communication protocols. Second, we introduce simple translation framework for analyzing similarities between aspects of natural language and emergent communication.
4.1 COMBINATORIAL. CODEBOOK FOR LARGER VOCABULARY
While VQ-VIB agents represent important step towards informatioonboundder emergent commu- nication, the specific architecture, as proposed by Tucker et al. (2022) is limited in an important way: agents may only communicate via one of the quantized vectors in their codebook. In contrast, we propose simple modification to the VQ-VIB architecture to allow agents to select n 2 vectors from the codebook before concatenating them into a single message, as depicted in Figure 2 Given meaning, m, speaker computes means and variances (Mi, σ4) (z) from which latent, continuous vectors 2; are sampled, each in R2/n, These continuous vectors are then discretized via the standard vector-quantizatiion procedure of returning the nearest entry, using a codebook of size k. Lastly, the discrete vectors ([c1,.., .., c..)) are concatenated into single message, E _ (Concatenating mul- tiple quantized vectors is used Product Quantization literature (Jégou et al. 2011 Baevski et al.) 2020), but without the Gaussian- based sampling that forms the variational backbone of VQ-
While architecturally simple, this change over Tucker et al. 2022) increases the number of unique messages the speaker may emit from & to kn without increasing the number of network parameters. Indeed, for k: = 1024; n = 4, as used in some of our experiments, one would need a codebook with more than 10 entries achieve similar vocabulary size with that this change dramatically increased the informativeness of learned communication. Lastly, we note that even for 1, we may use the same variational upper bound complexity by penalizing divergence from a Gaussian distribution.
1. In experiments, we found
4.2 HUMAN-AGENT TRANSLATION
One of our goals in this work is to quantitatively analyze similarities between BC signals and natural language as a function of the complexity of the EC system. In particular, we leverage the fact that VQ-VIB agents communicate via discrete signals that embedded in continuous space (see also Tucker et al. 202T). much like grounded word embeddings. We therefore study the similarity between the VQ- VIB EC embedding space and word embeddings from natural language.
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(a) VG: animals.plants, Labels: bird (20), duck (14)
(b) VG: people; Lahels: man (24), person (2), human (1), male (1)
(c) VG: home;
Lahels: couch (21), sofa (9)
Examples from the ManyNames dataset, with Visual Genome (VG) labels and English naming response (and counts). The topname of each image is shown in bold.
Figure 3:
To this end, propose simple translation mechanism from natural language to EC. We assume access to a pre-trained neural speaker that outputs communication vectors, and to a dataset of images with natural language labels descriptions. Natural language descriptions vectors using word-embedding methods, e.g., word2vec or Glo Ve (Mikolov et al.] [2013] Pennington al.) 2)14). One can construct translation dataset, associating EC vectors and natural language embeddings, by (i) mapping each image to its label's word embedding, and (ii) passing the input image through an EC speaker map it to EC vector. Using this dataset, we train a least-squares linear model to map from natural language to EC. In general, non-linear translation models may be used as well; we leave exploration of such translators to future work.
be represented real
In evaluation, we test the translatability of an EC system by combining human speaker with pre-trained EC listener. That is, given a target image, the human speaker generates word which represented by its embedding; that embedding is translated an EC vector and passed to the EC listener, which then tries to pick the correct target image among all candidates. By evaluating the utility that this hybrid team achieves in this task we could assess the degree to which our EC agents are suited for communication and cooperation with humans.
5 EXPERIMENT DESIGN
We sought to measure the influence of complexity and informativeness generalizability and trans- latability in EC. In experiments, we trained agents self-play before evaluating them in novel set- tings or with simulated human partners
Dataset We used the ManyNames dataset (Silberer et al., 2020), consisting of 25,000 images of natural objects with human-generaaed labels. This dataset particularly useful in our context be- cause it is composed of two parts (see Figure examples): (i) Visual Genome (VG) data, labeling each image with with one of seven high-level VG categories ['animals plants', 'buildings', 'cloth- ing', 'food', 'home', 'people", 'vehicles"]: and (ii) free-naming data produced by approximately 36 native English speakers (the exact number varied due to filtering, see [Silberer al.) (2020) for details), yielding a rich naming distribution of 1763 human-generated labels. These data naturally reflect the naming patterns and varying abstraction-levels that appear in English, as well as other languages (e.g.. Malt et al. 1999). Additionally, these phenomena are consistent with the Informa- tion Bottleneck view of object naming systems (Zaslavsky et al.) 2019), that could settle on low- informativeness and low complexity (e.g., Figure VG) higher informativeness and complexity (e.g., Figure 4p. topname). Given that these images elicited different levels of communicative complexity, hoped that the dataset would similarly support variety of EC patterns.
Training setup We trained agents the Lewis signalling game depicted in Figure [ a speaker observed "target" image and produced communication while a listener observed C candidate im- ages, including the target image, and the speaker's 's communication and had to select the target image from the candidates. We used pretrained ResNet18 extract 512 dimensional features from all images before passing them to the agents (He et al.) 2016). In all experiments, we trained agents in self-play with no human supervision data. Agents were trained with fixed number of candi-
"Code for experiments available http://aaooyymoos..ppoossccccccc -iui-

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date images (typically, 2, unless using baseline inspired by Chaabouni et al. (2021b)) drawn from distinct categories. In some experiments, we trained agents with images from separate VG classes; at other times we used distinct topname labels. By training agents with candidates from distinct sets, we intentionally separated the utility and informativeness terms. Note that this differs from experiments by (Tucker al. (2022), who did not use distinct candidates, their informativeness and utility terms were maximized by the same policy.
Evaluation in novel settings and domains At test time, we evaluated team utility (accuracy in identifying the target image) for various C, without enforcing that candidate images were drawn from distinct classes. Both changes made the task more challenging: increasing C' forced listeners to identify the target among more distractors, while allowing candidates from the same class allowed distractors to be more similar to the target. In some experiments, we also tested agents' ability to generalize to new image types. In such cases, we split the ManyNames dataset into distinct training and testing sets, without overlapping image domains. To this end, for each image, we recorded the most common label (of the 36 responses), referred to as topname (see Figure for examples). We then randomly selected 20% of the 442 topnames in the entire ManyNames dataset and used all images with these topnames in the training set. The test set was generated by iterating over the remaining images in the dataset and retaining those for which no response (not merely that image's topname) matched training topname. For example, if the image of the duck in Figure a were in the training dataset, no images with any responses for "bird" were included in the test set. We found that this train-test split procedure created sets of similar sizes that were substantially semantically different.
Human- aagntt cooperation In our human-agent experiments, we tested the listener agent's ability to partner with human speakers, To this end, we used the free-naming English data to simulate a human speaker with the agents from the EC teams trained in earlier self-play experiments with dis- tinct topname categories. We generated a "translation dataset" by randomly sampling 100 images; the topnames of the images were converted to Glo Ve embeddings (Pennington et al.) 2014) and the speaker agent produced corresponding EC vectors. We then fit a linear translation model to map from Glo Ve embeddings to EC vectors; at test time, we measured the team performance when using the simulated English speaker, the translator, and the EC listener. To overcome random effects due to sampling so few images, during evaluation we performed 10 random trials for differently-trainee translators and reported the mean effect.
To evaluate the influence of the informativeness petemaatieeess-coplleittty tradeoff on general- ization and translatability in EC, varied \y the weight for informativeness in Equation (1) while fixing Av = and Ac = 0.01. Increasing A5 increases the pressure for informative communication beyond any specific tasks, which in turn may increase the complexity of the communication system. In addition, we varied n, the number of quantized vectors per message, because (as demonstrated in experiments) we found that such architectural changes had large effects on the informativeness of learned communication. As baselines for evaluating our adjusted VQ-VIB agent architecture, we considered both onehot- and prototype-basee architectures (Tucker et al., 2021; Chaabouni et al., 2021b). Details of agent architectures and hyperparameters are provided in Appendix A In all experiments, we trained 5 agents from scratch for random seeds (0 to 4).
Agent variations
RESULTS
Here, we focus on the results for the VQ-VIB agents. Comparisons to other architectures (reported in Appendix Cp corroborate Tucker al. 22022 's findings that VQ-VIB outperforms alternative EC
methods (see Appendix Table 2 and Figure 10)
6.1 BASIC REFERENCE GAME
First, we trained agents in simple reference game, with only two candidate images (the target and a distractor; C' 2) that were always drawn from distinct VG categories. This intentionally simple setting could be solved by learning a 7-element vocabulary (one for each VG domain) and correctly classifying each image. At test time, however, by allowing candidate images to belong to the same category (c.g., two vehicles) and by varying C. at test time (e.g., using 32 candidate images instead of 2). Results are shown in Figure First,
evaluated agents in more challenging settings

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(b)
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Figure 4: Basic reference game. (a) Measuring informativeness (as negative reconstruction MSE) vs. complexity shows the informativeness-ooppl Team utility in the reference game, as function of communication complexity. Using greater d and greater n induced more complex communication, which improved utility, especially when evaluated with more candidates (different curves).
tradeoff characteristic of IB systems. (b)
increasing A to encourage informativeness produces not only more complex and informative com- munication, as expected, but also greater utility. Second, increasing the combinatoriality parameter has similar effect of increased complexity, presumably by allowing agents to learn to combine quantized vectors into more meaningful representations. Lastly, in this setting, greater complexity, whether achieved by varying architectures and a greater range of A corroborate this trend, although VQ-VIB reached greater complexity and utility than other methods (Appendix C Table 2)
Y supports greater team utility, Results using other speaker
6.2 GENERALIZING TO OUT-OF-DISTRIBUTION INPUTS
In second suite of self-play experiments, evaluated VQ-VIB more challenging gener- alization task: testing agents on out-of-distribution (OOD) image classes We constructed train-test splits explained in {5| agents were trained on 20% of the data and tested on images that did not share labels with the training set. As before, we trained agents with C' = 2 and with images drawn from distinct categories (this time, distinctions by topname labels), but tested with varying num- bers of candidates, without requiring candidates to be drawn from distinct categories. Thus. in our hardest evaluation settings, agents needed to simultaneously generalize didates, drawn from non-distinct categories, which were not seen during training. Here, we present results for n 4; see Figure 11 Appendix for other values of n.
larger number of can-
Because informativeness, in contrast to utility, encourages agents to develop a shared understand- ing beyond specific task requirements, we expected that increasing \, in training would improve the agents' ability to generalize to OOD inputs. Figure [5 confirms this intuition, showing that utility, as measured by the listener's accuracy identifying the target image on OOD classes, increases with complexity greater X yields greater complexity). ANOVA analysis finds that correlations were significant (p 0.05; 4.6, 8.7,9.6) for C 2, 16, and 32, respectively. Note that each random trial used different agent initializations as well as different train-test splits. Interestingly, even as performance on OOD inputs increased with complexity, it increased slower than utility on in-distribution images, as seen in Figure 5p, which shows the mean difference between utility on the training and testing splits of the dataset. The growing generalization gap suggests limit OOD generalization, war- ranting further exploration in future research. However, even with this gap, VQ-VIB agents achieve substantially better OOD performance compared onehot- and prototype- based agents (Figure 5 for highlighted results and Appendix for complete comparisons.)
vary A1 0.,0.1, 1,, 1.5, 2, 3, 10) (as explained in $
Next, in order to gain more insight into the emergent semantic structure in VQ-VIB and how may facilitate OOD generalization, we visualized the EC vectors from training and OOD images (Figure (). To get a sense of the images associated with each EC vector, we iterated over the first 100 examples from an agent's training and testing set. Within each set, recorded the speaker's output for each image and averaged the EC vectors across images with the same topname. We thus obtained the average EC vector for images of, e.g., "skater" "steak." In general, we found that vectors that are close to each other often encode the same t topname, i.c. they can be treated as synonyms, and therefore averaging simply reduced noise for visualization. Using 2D PCA (Jolliffe

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(2h.0
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060 098.189
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Figure 5: Generalizing to out-of-domain inputs. Listener accuracy function of complexity for OOD images improved with complexity (a) but the gap in performance between in- and out- of-distribution images grew (b). Compared to other architectures (for C 32), VQ-VIB achieved greater OOD utility. Results generated for n = for VQ-VIB while varying λς-
Train OOD EC
skater
bowtle SaLLAAT propeller
0.8
PCA 2
cabrfit
melooave
moped
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build
a dutglle
beer knife
fork
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tub
spopula
spcon
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steak
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Eng. topname
C= 32
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Complexity (nars)
Figure 6: 2D PCA of EC vectors for training (blue) and OOD (red) images at complexity that roughly matches English (2.1 nats, d 0.5).
Figure 7: Performance of human-agent teams using GloVe to EC translation. Performance flattened after passing English complexity.
Cadima 2016). Figure ② shows these vectors with their associated topname for images in the training set (blue) and the OOD test set (red). The agents learned meaningfully structured semantic space, much like word embeddings in natural language. For example, in the training set, communi- cation about food falls in one cluster, while furniture is in another. Remarkably, this semantic space also enables high performance on the OOD images because communication for held-out classes like "fork" or "microwave" can be interpreted based on its location in the pre-trained space. Visualiza- tion of the EC vectors at different complexities (Appendix B) indicates that VQ- VQ-VIB consistently forms structured semantic spaces, although the amount of information conveyed in that space varies.
Lastly, we tested whether using more candidates at training time enabled better OOD generalization. Chaabouni al. (202Tb) found that training with more candidates improved test-time performance, but they did not investigate the effect of varying C on OOD inputs. Table T shows that increasing À led to greater improvements in OOD generalization compared to increasing the number of candidates C at training time (although varying C' is more general than varying A in that it does not assume our information-tee -thooreticc framework). We believe this occurs because increasing at training time does not encourage agents to construct shared representations that are not task-specific, in contrast to increasing Å₁. Furthermore, increasing during training (for the same number of epochs) has substantial overhead in runtime, while similar or even better performance can be achieved much faster by increasing \ instead. For example, training with fast as training with C 32, while also achieving the best OOD performance.
and X about twice as
6.3 ENGLISH- EC TRANSLATION
So far, we found that encouraging informative communication enables teams VQ-VIB agents, op- crating in self-play, to better generalize to harder tasks and novel inputs. In this section, we studied the extent to which these agents can also usefully cooperate with humans by evaluating the translata- bility between English and EC. To this end, we measured team performance for simulated English speaker and an BC listener by selecting random images from the ManyNames dataset, looking up the GloVe embedding for each image's topname (the most common name provided by the par- ticipants in the ManyNames English naming task), translating the embedding to EC via the linear translator (see (5), and then passing the translated communication the trained EC listener (as well as candidate images). Team utility, for varying agents and C, is plotted Figure 7 At low complexity values, team performance exhibited similar trends prior results: increasing complex-
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Table 1: Mean (std. err.) utility evaluated on OOD images, for n 4. Increasing xy allowed better generalization to novel inputs, while increasing C' during training was less effective and slower.
Test C (outtof-diitribbution)
Train C
X 2
16
32
Comp. (nats)
Time (min)
0 0.64 (0,08)
0.16 (0.58)
0.09 (0.03)
0.60 (0.37)
17 (0.4)
2
0.91 (0.01)
0.51 (0.04)
0.37 (0.03)
2.73 (0.03)
16 (0.4)
0
0.89 (0.02)
0.38 (0.03)
0.28 (0.02)
2.04 (0.06)
49 (2.1)
16
0.93 (0.01)
0.53 (0.03)
0.41 (0.02)
2.93 (0.08)
50 (1.8)
0
0,87 (0.02)
0.37 (0.04)
0.25 (0.02)
1.98 (0.07)
101 (8.1)
32
0.92 (0.02)
0.48 (0.03)
0.35 (0.02)
2.88 (0.08)
109 (2.7)
ity and informativeness supported greater utility. However, at roughly 2 nats, team performance stopped improving even as EC complexity increased. This cutoff is particularly noteworthy because it nearly equals the complexity of the English naming system associated with this visual objects in the ManyNames dataset, calculated to be 1.9 nats using the MINE estimator (Belghazi et al. 2018) for the mutual information between English topname labels and images. A statistical like lihood ratio test of two nested linear models (one for utility vs. complexity, and another for a pair of linear models for utility vs. complexity, fitted separately for complexity greater or less than 2 nats) was significant (p < 0.005), confirming a plateauing behavior. Thus, we found that team utility is bottle- necked by the complexity of the English speaker, and that further increasing complexity in self-play affords no benefits. Appendix [ iincuues similar results for translators fitted with more or less data; growing the dataset helped to some extent, but performance generally plateaued at 2 nats.
CONTRIBUTIONS
In this work, we explored the influence of informational constraints on two important properties of emergent communication (EC): generalization to harder tasks, including novel inputs, and transla- tion between EC and natural language. We suspected that most EC methods fall short of achieving these desired properties because they focus only on maxitiimaxiiizttion, without considering infor- mational constraints that are believed to shape human languages. We focused here grounded meaning representations, and hypothesized that trading off informativeness and communication complexity, as suggested by the Information Bottleneck framework for semantic systems (Zaslavsky al. 2018, Zaslavsky 2020), would lead to EC systems that are better suited for human-like generalization and human- agent cooperation. To test this, we extended the VQ-VIB method for cooomattio-bounded EC agents (Tucker et al. 2022) to enable a richer and more scalable vocab- ulary structure. In addition, proposed simple translation mechanism between EC vectors and natural word embeddings, and used that to simulate human-agen communication in a cooperative image discrimination game.
Our results generally support our hypothesis and further show that VQ-VIB outperforms onchot- and prototype-based EC methods. In particular, showed that: (1) encouraging informativeness allows agents to better generalize to more challenging tasks and nut-of-distribution inputs; (2) the structure of the emergent VQ-VIB communication vectors encodes some semantic similarities and facilitates open-domain communication, similar to word embeddings in natural language; and (3) performance for teams of simulated English speakers and trained EC listeners improves with the complexity of the EC system, but only up to the complexity level of the English naming system. These results ceeemaatiiieesssooplleeeeeee
suggest that taking into account the IB maximizing utility, may support both self-play performance and human-agent interaction.
tradeoff in EC, in addition to
Future work could build upon this framework in many directions. While we have shown the impor- tance of controlling informativeness and complexity in EC, further exploration of these constraints may involve novel neural architectures, as well as additional domains and tasks. In addition, while we took step towards measuring connections between EC and representations of natural language, more sophisticated translation mechanisms could uncover deeper relationships. Lastly, given the rich literature that has found IB-efficient human communication in many domains, and im- proved combinatorial codebook within VQ-VIE VIB, future work might consider connections between EC and different parts of speech.
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A TRAINING HYPERPARAMETERS
Here, we report the hyperparameters used in our experiments. In general, hyperparameters were chosen to maximize self-play performance on the training task, without considering performance on more challenging validation setups (e.g., with more distractors or duttt--distriuution images). Thus, were able to fairly test how successfully learning one task enabled agents to generalize to other settings.
We used a Resnet18, available through Vision that had been pretrained on ImageNet, for ex- tracting -i2-dimensional features from images. These features were pre-calculated and cached at the start of all training runs. We trained VAE with symmetric encoder and decoder with fully connected ReLU layers dimensions 128, 64, and 32 to reconstruct such features. Thus, in ref- erence games, the speaker and listener actually observed the result of taking image, passing it
12

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through the Resnet18 feature extractor, and then passing it through the VAE reconstruction to get slightly noisy version of the true features.
All speakers connected ReLU layers, feeding into the communication "head." Details of the particular head architectures are included below. The communication decoder comprised 2 fully-connected ReLU layers with hidden dimension 64, mapping from communication vectors to reconstructed images. The listener agent comprised two separate linear layers that mapped from communication image features to 16 dimensions; the listener's output was computed via the softmax of the cosine similarity between the communication embedding and each image's 's embedding.
parametrized with common backbone comprising three, 64-dimensional fully-
The decoding loss was calculated as the mean squared error (MSE) between the decoder's output and the speaker's observation. The classification loss was the categorical crossentropy of the listener's output and the onehot label for which candidate image was the target. Agents were trained with batch size 64 until convergence. the VG domain experiments, we trained agents for 20,000 batches; in the t topname experiments, we trained agents for 100,000 batches.
A.1 VQ- VIB SPEAKER
The VQ-VIB Speaker generated communication vectors by first sampling in a 64-dimensional latent space using the reparametrization trick for sampling from Gaussian and then quantizing to of the vectors in the codebook. We parametrized VQ- VIB agents with 1024 codebook entries.
Traditional VQ architectures sometimes suffer from "codebook collapse" in which only small fraction of quantized vectors are used. We found that simply using a small positive Àc = 0.01 overcame such issues, likely because regulating complexity increased the stochasticity of encodings, preventing premature settling to local minima.
As in standard VQ architectures, we set the hyperparameter 3 to tradeoff between the committment and embedding losses. In experiments for Ar > O, we set / = 0.25, the default value. In exper- iments for X 0, we set 8 0.01; this lower value allowed prototypes move more, which appeared necessary given the weaker training signal when the informativeness loss was not present.
In all experiments, we used an Adam optimizer with default parameters
A.2 ONEHOT SPEAKER
Although the results in our main paper focused on VQ-VIB agents, we conducted some baseline ex- periments with onehot-based communication (results reported Appendix C.. The onehot speaker produced onchot vectors by passing through "hard" Gumbel-softmax layer. We used a 1024- dimensional Gumbel-softmax layer to allow for up to 1024 unique messages.
Training agents with default settings for this layer often failed to achieve tetter-than-randon chance; the speaker often devolved to outputting the same message for all inputs. We explored several methods for addressing such failures, including losses for the message entropy (as discussed in Eccles et al. (2019), among others) and increasing the temperature parameter of the Gumbel softmax layer (as explored in reference games by Chaabouni al. (2021a), among others). Sweeping over entropy weights from 0.0001 to 0.1 (at powers of 10), and temperatures in the set [0.1, 1. C, 10.0, 20 0, 50.0 0] we achieved the best self-play results by penalizing the message entropy with weight 0,001 and setting the temperature to 20 throughout training, while setting λ = O. We note that onehot-based communication appeared much more sensitive to hyperparameter tuning than VQ-VIB agents.
Agents were trained using an Adam optimizer with default hyperparameters, except for the learning rate, which we set to 0.0001. This rate led to higher self-play scores compared to 0.001 (the default, which led to unstable training that caused communication collapse) and to 0.00001, which never achieved greater- greater-than- thaatthhaaaae random scores.
A.3 PROTOTYPE SPEAKER
Similarly to using onehot agents as a baseline, we conducted additional experiments with prototype- based agents, proposed by Tucker et al. (021). Such agents used internal Gumbel-softmax layer
13

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spatula
OOD EC
2
To doMegut
food
Trnsleted Eng.
donut micld speort speor
PCA
PCA
steoic
cabi Calr CANI tup
equarium
propeller
tub
buildoon
propeller
moped
skconeatter
wall
rest Irant moged
restauraffoped
coat
coat
PCA 1
PCA
(a) Train vs. OOD; Comp. = 1.5 nats
(b) EC vs. Translated English; Comp. = 1.5 nats
copt skater
skater Skater
OCD EC
bowtle
Translated Eng.
bowtie restaurant
moped
PCA
wall
cabiriipir
nnflowvee
propeller
moped
wall cabi masnlabir
wall eesturrant
propelter
moped
beer knita
beer
build
dulille
building
ibuok
donut food
-2-
dopped
doseed
-1
-2
2
FCA 1
PCA
(c) Train vs. OOD; Comp. = 2.1 nats
(d) EC vs. Translated English; Comp. = 2.1 nats
fork
food
steak
donut
steak
,000 EC
beer
donut
food
knife
spatula
PCA 2
tub
pan
moped
monaa
EC
colabinet Cakiinet
Translated Eng.
cat
truck
chair
-2-
woll
DOWB
Restegsant
skater
wall
restaurant
Festaskgitt
skater
1
3
4
PCA
PCA
(e) Train OOD; Comp.
2.7 nats
(I) EC Translated English; Comp. 2.7 nats
propeller
propeller
stater 5.0
maped
neapedt moped
steak beer
steak beer
PCA
0.0
tree building
truck coat
aquarium tub
bowii snapfand
0.0
Suildiin@/kkiiiilll restatiaanaaan
truck Coat
bowtle food
food
donut
dorut
restaurant
Ban
tub ch&at
-2.5
Tain EC
-2.5
chefaloini Waltinet
EC
wasbinet
Eng.
2
(g) Train vs. OOD; Comp. 4.6 nats
(h) EC vs. Translated English: Comp. 4.6 nats
Figure 8: 2D PCA of communication for OOD inputs (left) or translated GloVe embeddings (right) at various complexity levels (different rows).
with 1024 units to select a trainable prototype in 64-dimensional communicative space (to match VQ-VIB). We used default parameters for all settings, except the Adam optimizer learning rate which, as for onehot agents, we set to 0.0001. We also set A 0 to encourage highly informative (and complex) communication.
B APPENDIX: VISUALIZATION OF LEARNED COMMUNICATION
In this section, we further explored visualization learned communication via the visualization method used for Figure 6 wherein recorded the mean EC vector for images associated with different topnames. In Figure & we plotted the 2D PCA of EC for in in-distribution images (blue), EC for OOD images (in red), and the result of translating English words into the EC space (in green).
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Table 2:
Team utility in VG reference games, for different speaker types. Onehot and prototype
agents converged to lower-complexity and lower-utility communication than VQ-VIB agents.
Test C
Speaker A
2
16
32
Complexity
0.1
0.63 (0.05)
0.07 (0.01)
0.05 (0.01)
0.27 (0.13)
1.0
0.65 (0.04)
0.14 (0.02)
0.08 (0.01)
0.39 (0.11)
Onchot
10.0
0.87 (0.01)
0.30 (0.01)
0.18 (0.01)
1.27 (0.03)
100.0
0.85 (0.02)
0.26 (0.02)
0.15 (0.02)
1.14 (0.12)
0.1
0.91 (0.01)
0.38 (0.03)
0.23 (0.03)
1.32 (0.07)
Proto.
1.0
0.92 (0.00)
0.43 (0.01)
0.26 (0.01)
1.71 (0.04)
10.0
0.93 (0.00)
0.50 (0.01)
0.33 (0.02)
2.01 (0.10)
100.0
0.95 (0.00)
0.60 (0.02)
0.42 (0.01)
2.26 (0.04)
0.1
0.88 (0.01)
0.31 (0.01)
0.17 (0.01)
1.11 (0.02)
VQ-VIB
1.0
0.95 (0.00)
0.59 (0.00)
0.44 (0.00)
1.89 (0.02)
10.0
0.99 (0.00)
0.87 (0.00)
0.80 (0.00)
4.51 (0.11)
Across models of varying complexity, we consistently found that agents, which had been trained on images of walls, dressers, steaks, etc. (but not cribs, spoons, forks, etc.) learned a semantically- meaningful communication space. Words for household items like "dresser" and ""cabinet" are near each other, while foods form another distinct cluster. Furthermore, this semantic space generalized to held-out classes well. An image of apple, for example, induced similar communication to images of steaks, donuts, and food in the training set. This explains how agents were able to generalize to held-out classes well: the form of communication vectors encoded information.
Inspection of the translated GloVe embeddings gives sense of what EC listeners observed in trans- lation experiments. While admittedly imperfect, the linear translator model was largely able to locate translated communication in the right semantic area. For example, in Figure [ d. the trans- lated EC for "donut" and "food" are not perfectly aligned with EC for donuts or food, but the two representations are still near each other.
C APPENDIX: OTHER COMMUNICATION ARCHITECTURES
Although the main focus of our work is on the relationship between information-theorete proper- ties of EC and generalization or translation (and therefore independent of the exact form of com- munication), we performed some experiments with onehot- and prototype-base (the architecture introduced by Tucker et al. 222 communication. Results with such agents reinforced the main findings of our paper establishing a link between complexity and generalization, regardless of the specific speaker architecture. Further, our results indicated that VQ-VIB agents enabled far greater control over communication complexity compared to onehot prototype agents.
We replicated the VG experiments from Scction Teams were trained with C = 2. with candidates drawn from distinct VG categories, and tested with varying C for candidates with no distinction enforced. Results are included in Table including results for VQ-VIB (for n = 4).
The most important result in Table 2 is that the trend between greater complexity and greater utility holds for all agent architectures. That is the primary focus of this work, and this table indicates that specific neural architectures do not affect this finding. Beyond that primary result, secondary effects from different speaker types are visible. First, for the same Λy. onehot agents tended to converge to wwwplccoyl and lower-utility communication than VQ-VIB agents. This corroborates findings from Tucker et al. (2022) that the VQ-VIB architecture appears to have a natural inductive bias towards more complex communication. Second, onehot agents seemed to require more complex communication for the same utility as VQ-VIB agents. For example, onehot communication for A = 10 achieved comparable utility to VQ-VIB for \n = 0.1, but at 1.27 nats instead of 1.11 nats for VQ-VIB. Third, even for very high År. we were never able to induce utility communication for onehot agents as VQ-VIB agents, and in fact high a appeared to worsen performance. Lastly, prototype agents to large extent reproduced the trends present in VQ-VIB agents, although still at lower complexities. Given their underlying similarities of using discrete representations in continuous space, this result is unsurprising.
complex high
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Table 3: Team utility for OOD for Topname agents, for different speaker types and selected Ay.
Across architectures, increasing A led to more complex communication that generalized better, but VQ-VIB V-B (for n 4) achieved the best performance.
Test C
Speaker
X
2
16
32
Complexity (nats)
0.1
0.53 (0.05)
0.07 (0.00)
0.03 (0.00)
0.19 (0.25)
1.0
0.71 (0.08)
0.17 (0.05)
0.10 (0.04)
0.92 (0.35)
Onehot
10.0
0.78 (0.02)
0.25 (0.03)
0.16 (0.00)
1.30 (0.12)
100.0
0.77 (0.01)
0.24 (0.00)
0.15 (0.00)
1.26 (0.15)
0.1
0.86 (0.03)
0.36 (0.03)
0.24 (0.04)
1.19 (0.08)
1.0
0.89 (0.00)
0.44 (0.02)
0.29 (0.01)
1.53 (0.14)
Proto.
10.0
0.92 (0.00)
0.53 (0.05)
(0.05)
1.91 (0.05)
100.0
0.93 (0.02)
0.58 (0.07)
0.43 (0.06)
20.060
0.1
(0.23)
0.29 (0.03)
0.16 (0.01)
1,55 (0.23)
1.0
0.91 (0.02)
0.51 (0.06)
0.37 (0.04)
2.73 (0.06)
VQ-VIB
10.0
0.97 (0.00)
0.80 (0.06)
0.71 (0.08)
4.46 (0.51)
100.0
0.98 (0.01)
0.80 (0.03)
0.72 (0.04)
4.46 (0.24)
10-
Prote
53
12
12
10
231
18
(a) C
(b) C' 16
(c) C 32
Figure 9:
utility as function of x (plots for different C). For all architectures and C, increasing N first increased utility before starting to plateau.
Similar trends appeared in topname experiments with onehot- and prototype-based communication using the same 20% generalization evaluation procedure. First, for various A₁. we found that VQ- VIB converged to greater complexity and utility than other architectures (Table 3 and depicted graphically in Figure Dp. Second, as in the VG experiments, we found that all agent architectures achieved roughly similar utility for the same complexity, but that non-VQ-VID agents failed to leamn as complex communication (Figure I0.. (Interestingly, prototype agents seemed to learn more complex communication than onehot, but not complex VQ-VIB.) Note that simply increasing A failed to produce the desired effect for onehot agents, which converged to similar behavior for A 10 and A, 100. Jointly, these results indicate that tuning X, an effective mechanism for increasing the complexity and informativeness of communication, and that VQ-VIB was best able to learn the highest-utility communication.
Lastly, translation performance with onchot-based agents was predictably poor, with utility for C = 2 never exceeding 75%, even for \ 100. Such failure can be attributed two lim- itations of onehot agents. First, as already shown in earlier experiments, onehot agents learned lower-complexity communication than VQ-VIB agents. which limited the maximum utility. Sec- ond, specifically in the context of translation, communicating via onehot vectors inherently limits the semantic relationships between messages. VQ-VIB agents, with discrete representations a continuous space, could leverage the alignment dataset to learn transformation of the whole space. Conversely, because every onchot vector is by definition orthogonal to every other onchot vector, translation necessarily failed to capture relationships between messages.
Ultimately, based on these results, we omitted discussion of onehot- and prototype-based communi- cation from the main paper. Our primary focus was the effect of different complexity and informa- tiveness on generalization; given the consistent results across architectures but more limited range of complexities for non- VQ-VI agents, VQ-VIB agents simply presented more interesting results.
16

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1.0
1.0
In-Distiiuatin Uttlity
0.6
0.6
000
0.2
C=
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Figure 10: In- and -uiti--distiibuttinn utility for onehot (top), proto (middle), or VQ-VIB agents (bottom). All agents exhibited similar utility for the same complexity, but VQ-VIB agents learned far more complex communication, which allowed them to perform better in harder tasks.
17

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Figure 11: Team performance on in-distribution (a) and out-of-distribution (b) images, for various (different rows). The general complexity utility tradeoff remained the same, but using greater allowed greater complexity.
D APPENDIX: COMBINING QUANTIZED VECTORS
In the main paper, we primarily presented results generated for VQ-VIB agents with 4. In Figure [T] we include results from our topname experiments, generated for n = 1,2, and 4. In general, we found that using greater n increased communication informativeness and complexity, which in turn supported greater utility. Most importantly, across n,, we found a similar relationship between utility and complexity. Thus, we view this architectural change of using larger n as allowing greater control over the complexity and informativeness of communication (which, as we discuss in main paper, is important for downstream tasks).
E APPENDIX: VARYING TRANSLATION ALIGNMENT DATA
In Section 6.3 we presented results for a simulated English speaker, with communication translated for an EC listener. The translator was linear transformation, fitted with N randomly-drawn ex- amples from the training dataset. In the main paper, we presented results for N 100; here, in present results for various N.
Figure 12
Ultimately, while increasing N helped translation somewhat, the general trends from the main paper held true. That is, as EC complexity increased from 0 to just over 2 nats, team utility increased, but at greater complexity values, team performance was bottlenecked by the English speaker. Furthermore, beyond certain value, increasing N further did not appear to improve performance, as demonstrated by the nearly identical performance for N = 100 and 1000.
18

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Figure 12: Utility of human-agent teams using English GloVe to EC translation, for various transla- tion dataset sizes. Increasing the dataset size helped up to a point, but performance plateaued around 2 in all cases.
19


Reviews:
[{"review_id": "v40ZFMGiW2E", "reviewer": "Reviewer_3hpH", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "", "summary_of_the_paper": "The authors propose some extensions to existing work on VQ-VIB architecture for modeling EC with a more explicitly information theoretic framework.  Namely, they extend the number of tokens which are chosen, and in doing so combine two threads of EC research -- existing VQ-VIB, and more traditional RNN/sequential type architectures.  In a set of experiments on English, the authors vary aspects of the model such as informativeness, showing among other things that increased informativeness is associated with better test time generalization, up until the point where it matches or exceeds that of the communication partner's language.", "summary_of_the_review": "Overall, I am still undecided on the overall merit of this paper.  On one hand, there is modest modeling contributions, and the experimental setup is unlike any I recall seeing before in EC.  The connections to the information theoretic complexity of English (of this dataset) is surprising in that it closely approximates previous attempts to calculate this number.\n\nOn the other hand, the formal presentation is messy, and the experiment setup seems to deviate strongly from standard EC assumptions.  Is it EC if one half of the protocol is derived from word embeddings of image labels?  Or if direct supervision is injected between the speaker and decoded output?  I have a hard time finding takeaway lessons that are both nonobvious and apply to the field more broadly.  The discussion contained within the paper does not go a long way towards reconciling this design with most existing EC work.  Similarly, this task seems setup specifically towards noun communication, but to what extent would many of these experiment choices work on a more general task with multiple parts-of-speech?  \n\nAt a minimum, for a paper claiming that most existing EC methods fall short of achieving desired properties in ways that this work presumably does not, it could obviously benefit from direct comparison with such methods.  Do existing works not also manipulate the same factors?  Informativeness is increased here through means like scaling factors on loss terms or increases in the vocab size and number of words communicated, but existing work also explores the effect of varying such things, albeit more removed from a formal information theoretic formulation.  I'm not sure a comparison to prototype style EC is sufficiently representative of EC methods in general (conclusions are phrased wrt to these more general methods)", "strength_and_weaknesses": "The main contributions of this work are likely (1) the extensions to VQ-VIB, and (2) a sort of evaluation paradigm that involves combining learned EQ representations with word embeddings into the same communication game.  The VQ-VIB extension, generalizing the model from a single \"word\" to multiple words, is straightforward, but important.  It is easy to imagine how this could lead to more direct comparisons with traditional EC models, or how the decoder could been sequential instead of fully connected, such that there would have been more incentive for the model to learn a more compositional protocol.  At any rate, it is a necessary extension for moving towards any real form of human-like EC, where a message is composed of multiple words, where meaning is conveyed not just in the words chosen but by their order.\n\n\nThe second contribution, the experimental setup, is harder to evaluate because there are so many unorthodox choices going on, and there is not consensus in what assumptions are aligned with the goals of EC (or constitute EC).  In my mind, many of these choices make this work more about grounding between two models, or more like unsupervised machine translation, than proper EC.  This makes it hard for me to evaluate the proposed findings as such.\n\nAnother comment on the modeling -- I was surprised to see the decoder is trained in a reconstruction style objective using an MSE loss, rather downstream from the classification loss on the signaling game.  What was the motivation here?  Is it reasonable in an EC setting to assume a connection between the speaker observation and the decoder on the other side of a learned protocol?  Doesn't this reduce the EC task substantially?\n\nI found some of the formal descriptions to be a bit messy.  m as a meaning and not a message / c as a communication and not a context/content, as is the case for many EC presentations, was slightly disorienting.  The KL is denoted with D and not KL.  Then D serves double duty as the decoder.  The lines are blurred between minimizing objectives over single m/c and the set of Ms/Cs (not defined).  Then c does double duty as indexes into the set of candidates.  Overall there is a lot of poorly chosen terminology in Section 3.\n\nI would also argue that some of the preliminary discussion dealing with the complexity of the English naming system have less to do with a general property of English, and more specifically with properties of the dataset and the granularity of the naming used there.  It was not clear to me if the authors agree with this point, since some of the latter discussion then seemed to explicitly mention the dataset when talking about this point.  ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "k2ba8J94MY", "reviewer": "Reviewer_QWC4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "", "summary_of_the_paper": "This work explores the relationship between emergent communication informativeness and complexity, hyperparameters of the vector-quantized variational information bottleneck (VA-VIB) method of training EC agents, and downstream metrics like generalizability and translatability with human language.\nIt finds that as the informativeness and complexity of emergent communication increases, it correlates with increased generalizability and translatability.\nIn support of these experiments, the paper extends VQ-VIB to multi-component messages and introduces a method for translating between embedding spaces of different languages (viz., English and an emergent language).\n", "summary_of_the_review": "This paper shows a handful of positive empirical results in support of the intuitions presented about hyperparameters in the VQ-VIB framework.\nNevertheless, the treatment of each contribution is relatively brief which does not communicate a sufficiently robust understanding of what is going or impress the importance of the contribution to emergent communication research as a whole.\nAs a result, the paper falls into a common category of emergent communication paper which presents an array of experiments and claims which, while interesting, are unlikely to be impactful.\n\n\n### Misc. Notes\n- Please use $\\mathbb{R}$ (`\\mathbb{R}`) for the real numbers\n", "strength_and_weaknesses": "### Strengths\n- [major] Empirical evaluation generally supports the hypotheses.\n- [minor] The extensions to VQ-VIB (translation and multi-component messages) are simple and appear to be effective.\n### Weaknesses\n- [major] The paper makes too many claims which means that each claim receives only a small amount of empirical evaluation, analysis, and discussion.\n  - I think the primary direction forward would be to take one of the claims, and take the whole paper to address it thoroughly.\n    Give a robust explanation of why that claim is important and how important future work will be enabled by the contributions of the paper (i.e., do not just say \"we could extend this with X, Y, and Z\").\n  - Example: \"increasing informativeness ... allows EC agents to better generalize to novel settings and more challenging tasks\" -- this is an interesting claim that would require a whole battery of experiments to prove convincingly.\n    Increasing the number of distractors in a signalling game does not convincingly count as a novel setting or more challenging task.\n    Instead, it would be best to see multi-step, multi-agent environments with different objectives that are not just a simple extension of the training environment.\n  - Example: \"increasing EC informativeness only improves team performance up to a certain threshold, corresponding to English informativeness-complexity tradeoff\" -- again, very interesting, but one would need to provide a robust characterization of what the trade-off is in English in the first place.\n    Furthermore, how do we evaluate this tradeoff beyond a specific environment and dataset; it would be good to first show this on synthetic data or in a theoretical setting before validating that it works the same way on real data.\n  - Example: \"the structure of emergent VQ-VIB communication vectors encodes some semantic similarities and facilitates open-domain communication, similar to word embeddings in natural language\" -- a very worthwhile direction that only receives a diagram and a paragraph rather than thorough treatment analyzing the structure of embedding spaces from multiple directions.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "SUUxm1AvEo0", "reviewer": "Reviewer_YuEu", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "", "summary_of_the_paper": "This paper studies how the informativeness and complexity of the emergent communication protocol in two-agent referential games affects the generalization performance and how these messages align with natural language. \n\nIn particular, they use the recently proposed framework of VQ-VIB, a variational inference approach using a discrete set of vectors (vector-quantitized) as possible messages for the agents. They extend this framework to allow passing $N$ vectors rather than just one as messages, and show that this allows the communication protocol to have more complexity.  They show that the \\lambda parameter in the VQ-VIB objective (associated with the **********informativeness********** of the message) also leads to the communication channel being more complex. Overall, the results suggest that greater complexity in protocol (with either multiple code-vectors or higher \\lambda) leads agents solving the referential game with higher performance. \n\nTo analyze the impact of the communication channel on out-of-domain generalization, they train agents for the same referential but using, at test time, a higher number of candidate images than trained for and/or candidates belonging to the same category of images. Overall they find that higher protocol complexity leads to agents that generalize better.\n\nFinally they test how aligned this emergent communication are with natural language by building a simple model that “translates” english words to one of the quantitized vectors and passing the associated image category word through this model to get a code and having the receiver agent predict based on this. They find that overall that this simple translation protocol works and that higher complexity in the channel helps but that, unlike with pure vector-based protocols, improvements with complexity plateu earlier.", "summary_of_the_review": "This paper proposes an extension of VQ-VIB to allow for more complex communication protocols and show that complexity is intrinsically correlated with how well the agents will do at the game. The analysis and experiments are convincing for the single task explored in the paper, but I would like to see this methodology applied more extensively to other tasks and settings. ", "strength_and_weaknesses": "- The paper is well-written and has a nice narrative. The idea of allowing multiple vectors is quite simple but works well in pratice, and the overall analysis of how informativeness and complexity in the channel affects performance is insightful.\n- The experiments with (simulated) human-agent are really interesting, and are nice approach to interpreting the \"abstract\" protocols these models learn.\n\nMy main criticism of this work, given the simplicity of the approach (that mostly just builds on top of the previously proposed VQ-VIB) I would like to see if it generalizes to multiple benchmarks and settings that emergent communication could be applied to. The authors only study one task/referential game for images, so it’s unclear if the findings surrounding the importance of channel complexity / applicability of their proposed approach generalize to more settings. ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "KvjRnTz6I5t", "reviewer": "Reviewer_cAgg", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "", "summary_of_the_paper": "This paper shows that explicitly controlling the trade-off between complexity, informativeness, and game utility in the emergent communication setup allows agents to discover languages with better generalization to harder/OOD tasks and better alignment with natural language. In order to demonstrate that, the authors study performance of a multi-symbol modification of VQ-VIB (which allows to control this trade-off) in a series of experiments, focusing on two setups: (a) referential games, and (b) human-agent translation.\n\nThe experiments performed on the ManyNames dataset demonstrate that:\n* higher communication complexity correlates with higher utility;\n* higher communication complexity correlates with better generalization to harder (more distractors) and OOD (unseen categories) settings;\n* up to a bound, increasing communication complexity allows higher utility in a simulated translation task.", "summary_of_the_review": "A very nice paper with interesting findings and high-quality experiments.", "strength_and_weaknesses": "Strengths:\n* The paper is clearly written and well-motivated, the findings are novel;\n* The experiments are convincing (taking into account Appendix C);\n* The simulated English translation task is very interesting.\n\nWeaknessess (mostly minor):\n* I believe the abstract can be more specific and indicate that the \"human-agent interaction\" is \"simulated human-agent interaction\" and \"English\" is eg \"English-based tags system\";\n* I am curious if there is a way for controlling the IB trade-off w/o imposing such strong architecture constraints. Eg, much of prior work operated on more standard architectures containing LSTM/GRU/Transformer blocks and producing sequentially generated multi-symbol messages. Are those compatible?\n* The last paragraph of S6.2 mentions that using more candidates at training-time also allows better generalization - however, at the expense of lower performance, e.g. training with C=16 is twice faster than training with C=32. Is it possible to elaborate on where the difference is coming from? Since the image encoder is pretrained and frozen, the image embeddings can be precalculated and the difference between selecting between 16 or 32 candidates should be small comparing to the rest of the computations?\n* While controlling the trade-off coefficient might be more efficient than increasing the number of candidates, the latter approach is universally applicable to any agent architecture. Is this worth highlighting?\n* I believe that the proposed adaptation of VQ-VIB to producing multiple tokens independently is akin to the widely used product quantization scheme (Product Quantization for Nearest Neighbor Search, Jégou et al; VQ-WAV2VEC: Self-supervised Learning of Discrete Speech Representations, Baevski et al). Perhaps it is worth having a reference.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject