The Curious Case of Representational Alignment: Unravelling Visio-Linguistic Tasks in Emergent Communication (2024)

Abstract: Natural language has the universal properties of being compositional and grounded in reality. The emergence of linguistic properties is often investigated through simulations of emergent communication in referential games. However, these experiments have yielded mixed results compared to similar experiments addressing linguistic properties of human language. Here we address representational alignment as a potential contributing factor to these results. Specifically, we assess the representational alignment between agent image representations and between agent representations and input images. Doing so, we confirm that the emergent language does not appear to encode human-like conceptual visual features, since agent image representations drift away from inputs whilst inter-agent alignment increases. We moreover identify a strong relationship between inter-agent alignment and topographic similarity, a common metric for compositionality, and address its consequences. To address these issues, we introduce an alignment penalty that prevents representational drift but interestingly does not improve performance on a compositional discrimination task. Together, our findings emphasise the key role representational alignment plays in simulations of language emergence.

## introduction human language bears unique properties that make it a powerful tool for communication. a wellknown property is compositionality: the ability to combine meaningful words into more complex meanings @xcite . the emergence of compositionality is studied extensively in the field of language evolution through human experiments (e.g. @xcite @xcite ). an important finding from this field is that the unique nature of human language can be explained as a consequence of biases for simplicity and expressivity imposed during continuous language learning and use @xcite . computational simulations of language emergence have also been used to study the emergence of linguistic properties (e.g. @xcite , and have seen a rising interest in the field of computational linguistics @xcite . here, compositionality in the emergent communication protocols is commonly measured through topographic similarity (topsim; @xcite . it measures the topographic relation between meanings and signals, conceptually it gauges whether similar meanings map to similar signals. this metric was first used in recent computational simulations by @xcite and has been used in a large body of work since. yet, the interpretation of linguistic properties emerging in simulations remains challenging, since language protocols used among artificial agents often show critical mismatches with known properties of human languages @xcite such as efficiency, word-order vs. case-marking biases, or compositional generalisation (see §2). consequently, it is evident that their learning biases and signal-meaning mappings differ from those of humans. this underscores the critical need to obtain deeper insight into referential games in the language learning setting @xcite . a possible explanation for these mismatches could stem from representational alignment, the degree of agreement between the internal representations of two information processing systems @xcite . to the best of our knowledge, representational alignment in emergent communication was first reported by @xcite , who measured the degree to which agents aligned their internal image interpretations (inter-agent alignment) by performing representational similarity analysis (rsa; @xcite . using rsa ( §3), they showed that agents establish successful communication artificially by aligning their internal image representations while losing any relation to the images presented (image-agent alignment), enabling communication about noise input even though they were trained on real images. as such, their communication protocol captured not conceptual properties of the objects depicted in pictures, but most likely focused on non-human-like spurious image features (e.g., pixel intensities). while inter-agent alignment is not a problem per se, the loss of imageagent alignment is problematic for two reasons. first, for emergent communication simulations to provide meaningful insights into the emergence of natural human language, agent image representations must be grounded in the content of the images. only then can we deduce what the agents communicate about and assess linguistic properties or their ability to generalise to novel concepts. second, emergent communication setups have been proposed to fine-tune pre-trained (vision-)language models, aiming to enhance machine understanding of natural human language @xcite @xcite . in this context, maintaining substantial alignment between representations and images is crucial for preserving mutual understanding between machines and humans. representational alignment, however, did not receive the necessary attention since a host of papers appeared after the findings by bouchacourt and baroni in which results on referential games were reported without taking rsa into account (e.g. @xcite @xcite @xcite @xcite . admittedly, some use attribute-value objects instead of real images as input. but importantly, in nearly all cases, neural agents must map inputs-whether attributevalue objects or image representations-onto agentspecific representations. therefore the problem of inter-agent alignment can always occur and is agnostic to the input type. although this warrants further analysis of earlier results, the field is already employing referential games in more complex simulations with real images (e.g. @xcite @xcite . this work addresses the understudied alignment problem in standard referential game setups used in emergent communication. we train reinforcement learning (rl) agents equipped with a recent vision module (dinov2; @xcite to communicate about images. in addition to evaluating the agents on ms coco @xcite image pairs, we evaluate on noise pairs and image pairs sourced from the winoground dataset @xcite . the latter is explicitly created to gauge visio-linguistic compositional reasoning abilities of vision and language models. we first confirm that effective communication in the referential game relies on inter-agent alignment and then move on to our contributions. first, we find a strong correlation between the degree of inter-agent alignment and the topsim metric. our second contribution consists of a solution to the alignment problem by including an alignment penalty term to the loss, resulting in equivalent communicative success and higher topsim whilst ensuring that the agents communicate about images instead of spurious features (figure 1 ). we then argue to start evaluating emergent communication protocols on more strict tasks that directly target the intuition behind popular metrics to obtain a clearer understanding of the protocols. overall, our results highlight the importance of representational alignment in simulations of language emergence and underscore the need to better understand the divergence in human and artificial language emergence. ## background most research in simulating emergent communication is modelled after the lewis signalling game @xcite with a speaker and a listener agent. the speaker observes a state (e.g., an image) and sends a signal to the listener who acts based on this signal. in the case of the referential game, this means selecting a target among distractors. both agents are rewarded for successful communication, meaning the listener points to the target object. the solution of this game requires the agents to have a shared protocol (i.e., an artificial language) which typically emerges when the agents learn based on trial and error over multiple games. this resembles how for humans, language learning and use impose constraints like pressures for learnability and compression that shape our language design @xcite . importantly, the emergent language in this setup is also shaped by biases resulting from, for example, the agent architecture, loss function, and learning protocol @xcite . the current work uses the referential game: a variant of the lewis signalling game extensively used to explore language evolution (e.g. @xcite @xcite @xcite @xcite . an important challenge in emergent communication is that artificial learners often do not behave the same way as human learners in experimental settings. some emergent protocols do not follow zipf's law and thus are anti-efficient unless pressures for brevity are introduced @xcite , others do not show the word-order vs. case-marking trade-off found in human languages @xcite . additionally, there is an ongoing debate on the degree to which the emergent languages allow for compositional generalisation @xcite . it has been suggested to introduce communicative (e.g., alternating speaker/listener roles) and cognitive (e.g., memory) constraints @xcite and use more natural settings to promote more human-like patterns of language emergence with neural agents @xcite . doing so changes the learning pressures to which the agents need to adapt and can recover initially absent linguistic phenomena of natural language in emergent languages (for a review see @xcite . an example of such work, investigating the wordorder vs. case-marking trade-off, has succeeded in replicating this trade-off for neural learners @xcite . their setup differs from other work in that agents first learn a miniature language via supervised learning, and then optimise it for communicative success via rl, resulting in emergent languages that share linguistic universals with human language. to enhance understanding of emergent communication in the @xcite decomposed the standard objective in lewis games into two key components: a co-adaptation loss and an information loss. in doing so, they shed light on potential sources of overfitting and how they might hinder the emergence of structured communication protocols. they demonstrated that desired linguistic properties (e.g., compositionality and generalisability) emerge when they control the listener's ability to converge to the speaker agent (i.e., control for overfitting on the co-adaptation loss). while the co-adaptation loss has parallels to inter-agent alignment, their work does not address the alignment between the agents' image representation and the input features, which we deem crucial in developing grounded communication protocols. another challenge in emergent communication is the disentanglement of the underlying meanings of emergent languages. earlier studies by @xcite suggested that agents assign symbols to general conceptual properties of objects in images, rather than low-level visual features. however, as previously mentioned, follow-up work from @xcite showed this is not always the case. they found that agents align their agent-specific image representations without developing a language that captures conceptual properties depicted in the images. more-over, agents lost any sense of meaningful withincategory variation where two similar objects in human perception (e.g., two avocados) were observed as maximally dissimilar for the agents. in response to these findings, recent studies have implemented sanity checks, testing whether trained agents can communicate about noise @xcite . however, to the best of our knowledge, there has been little attention to what we consider to be their main result: the alignment problem. ## representational alignment representational alignment is the degree of agreement between the internal representations of two information processing systems, whether biological or artificial. even though widely recognised in cognitive science, neuroscience, and machine learning @xcite , representational alignment has not seen much interest in the field of emergent communication, except for the work by bouchacourt and baroni who analysed the referential game using rsa. this metric measures the alignment between two sets of numerical vectors, for example, image embeddings and agents' representations thereof. in practice, it is calculated by taking the pairwise (cosine) distances between vectors of a set and calculating the spearman rank correlation between these distances. in this paper, we also use rsa to operationalise representational alignment. given the speaker image representations r s of the dinov2 input embeddings i and r l as the same images represented in the listener representation space, we compute the pairwise cosine similarity between the representations for the speaker s s and for the listener s l and calculate spearman's ρ between s s and s l . as such, this measures the degree of inter-agent alignment (rsa sl ) between image representations s s and s l , relative to their input. additionally, we use it to measure image-agent alignment between the speaker/listener image representations and the dinov2 embeddings (rsa si and rsa li ). importantly, alignment is agnostic to the type of input, being either images or attribute-value objects and can always happen when inputs are projected onto agent-specific representations. intuitively, a high inter-agent rsa sl value can be interpreted as agents with similar representations for similar images. importantly, this can have two causes: both agents' image representations either maintain a relation to the image input, or lose this relation. while the former is desirable, the latter means that the agents are not communicating about the same high-level image features but are likely communicating about non-humanlike spurious features. a low rsa sl value entails that the agents have developed different interpretations for the same image. while this may well be similar to the question of whether people have different perceptual experiences of colour @xcite , in the case of emergent communication, the agents should develop a grounded vocabulary with overlapping concept-level properties if we wish machines to have more natural understanding of human language. we use rsa 1) as a metric to reassess findings from bouchacourt and baroni and 2) as an auxiliary loss to mitigate the alignment problem and ensure that the agents communicate about image features. ## methods the standard referential game is used as provided by the egg framework @xcite . doing so ensures our findings are representative of the widely-used setup, rather than being influenced by specific design decisions. the game is implemented as a multi-agent cooperative rl problem where a speaker and a listener communicate to discriminate a target image from two shuffled distractor images. the speaker receives a target t and generates a message m of at most length l, using vocabulary v . importantly, the messages and symbols have no a priori meaning but are assumed to obtain meaning and become grounded during the game. once meaningful, the symbols are ideally combined in a structured manner to create compositional messages that express more complex meanings. using message m, the listener guesses the target t. communicative success is defined as @xmath0, meaning that the listener has correctly identified the target image among the candidate images. ## discussion in this work, we revisited the representational alignment problem in a common setup used in emergent communication and proposed a solution to this underrepresented problem. we corroborated earlier findings by showing that agents align their image representations and rely on spurious image features instead of human-like concept-level information @xcite . we then showed that inter-agent alignment strongly correlates with the commonly used topsim metric. our solution to the alignment problem involves an alignment penalty that forces the agents to remain aligned with the input features and mitigates the alignment problem without decreasing communicative success. finally, when agents are tested on more challenging winoground pairs we observed reasonable but lower performance regardless of whether image representations were similar to the image em-beddings or not. with this work, we hope that the alignment problem will receive more attention in the field of emergent communication, as is already the case in adjacent fields @xcite . ## conclusion this paper revisits the underrepresented alignment problem present in the referential game often used in simulations of emergent communication. specifically, we focused on the problem of increasing alignment between agent-image representations in combination with a decreasing alignment between the input and agent representations. we first confirmed that agents align their image representations while losing connection to their input, meaning that the emergent languages do not appear to encode human-like visual features. we then showed that, in the common setup, inter-agent alignment is related to topographic similarity, and argued that this renders topsim an indirect metric of the rate of inter-agent alignment. to further investigate the effects of alignment, we introduced an alignment penalty to mitigate the alignment problem and showed that the communicative ability on a strict compositionality benchmark did not improve, leaving the question of inducing compositional generalisation in emergent communication for images unsolved. our findings underscore the need to better understand the divergence between human and artificial language emergence within the prevalent referential setup and highlight the importance and potential impact of representational alignment. we hope that future work rules out or at least reports about representational alignment.