Challenge: Compositional languages leverage rules that derive meaning from combinations of simpler constituents.
Approach: They investigate the effects of one-to-many communication environment on emergent languages where a single speaker broadcasts its message to multiple listeners to cooperatively solve a task.
Outcome: The proposed model shows that broadcasting the speaker’s message to multiple listeners does not induce more compositional languages.

Similar Papers

Compositionality and Generalization In Emergent Languages (2020.acl-main)

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Challenge: a new study examines whether emergent languages possess compositionality . compositionality is a core concept in linguistics, but linguists' definitions assume full knowledge of primitive expressions and their combination rules.
Approach: They propose to use compositionality to combine expressions according to systematic rules to refer to composite concepts.
Outcome: The proposed language has compositionality, but it is not generalized, the authors show . they show that the more compositional a language is, the more easily it will be picked up by new learners .
Defending Compositionality in Emergent Languages (2022.naacl-srw)

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Challenge: a recent paper has suggested that compositionality is a key factor in language productivity, but some research has questioned this.
Approach: They argue that compositionality is essential for successful generalization . they run a two-agent communication game to test this hypothesis .
Outcome: The proposed results show that ANNs can generalize well even without compositional behavior . authors argue that the results are incomplete and weak .
Frequency & Compositionality in Emergent Communication (2025.emnlp-main)

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Challenge: Natural languages exhibit a universal tendency to resist regular patterns, developing idiosyncratic forms.
Approach: They investigate the relationship between frequency and compositionality in emergent languages . they use a referential game setting to manipulate input frequency through Zipfian distributions .
Outcome: The proposed method shows that the frequency distributions of the most frequent words resist regular patterns, resulting in less compositional structure.
The Emergence of Compositional Languages in Multi-entity Referential Games: from Image to Graph Representations (2024.emnlp-main)

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Challenge: Language Emergence research uses jointly trained artificial agents to solve a task.
Approach: They propose a multi-entity game in which targets include multiple entities that are spatially related.
Outcome: The proposed multi-entity game shows that the emergent languages exhibit a considerable degree of compositionality, but not over all features.
On the Correspondence between Compositionality and Imitation in Emergent Neural Communication (2023.findings-acl)

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Challenge: a study examining compositionality and imitation learning in a Lewis game demonstrates that it is difficult to imitate compositional languages.
Approach: They explore the link between compositionality and imitation in a Lewis game . they show that the learning algorithm used to imitate is crucial .
Outcome: The proposed model improves compositionality and imitation in a Lewis game . the study shows that compositional languages are easier to imitate .
Concept-Best-Matching: Evaluating Compositionality In Emergent Communication (2024.findings-acl)

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Challenge: Existing evaluation methods do not expose compositionality of emergent communication . compositionality is a trait that enables the construction of complex meanings from the meaning of parts.
Approach: They propose to find best-match between emergent words and natural language concepts to assess compositionality of emergentic communication.
Outcome: The proposed algorithm provides a global score and translation-map between emergent words and natural language concepts.
On the Spontaneous Emergence of Discrete and Compositional Signals (2020.acl-main)

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Challenge: Using a continuous latent space, we are able to train using backpropagation and show discrete messages nevertheless naturally emerge.
Approach: They propose a general framework to study language emergence through signaling games with neural agents.
Outcome: The proposed framework shows that discrete messages naturally emerge and that they are not compositional.
Emergent Language-Based Coordination In Deep Multi-Agent Systems (2022.emnlp-tutorials)

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Challenge: Pre-trained deep networks are the standard building blocks of modern AI applications.
Approach: This tutorial will introduce deep net emergent communication and discuss current shortcomings . participants will implement and analyze two emergentic communication setups from the literature .
Outcome: The presentation will cover various topics from the present and recent past, as well as discussing current shortcomings and suggest future directions.
Emergent Linguistic Phenomena in Multi-Agent Communication Games (D19-1)

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Challenge: a recent study examines the behavior of linguistic agents in a community-level setting . a linguistic continuum emerges where neighboring languages are more mutually intelligible than farther removed ones .
Approach: They propose a multi-agent communication framework for studying linguistic phenomena at the community level.
Outcome: The proposed framework can reproduce complex linguistic behavior observed in natural language . it can be used to study interactions between perceptually-enabled agents .
Emergence of Hierarchical Reference Systems in Multi-agent Communication (2022.coling-1)

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Challenge: a hierarchical reference system allows the selection of the most appropriate level of specificity for a given context.
Approach: They propose a hierarchical reference game to study the emergence of hierarchic reference systems in artificial agents.
Outcome: The proposed game shows that agents can generalize to new concepts . the hierarchical reference game is based on a simplified world .

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