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        <title>On the Correspondence between Compositionality and Imitation in Emergent Neural Communication</title>
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    <abstract>Compositionality is a hallmark of human language that not only enables linguistic generalization, but also potentially facilitates acquisition. When simulating language emergence with neural networks, compositionality has been shown to improve communication performance; however, its impact on imitation learning has yet to be investigated. Our work explores the link between compositionality and imitation in a Lewis game played by deep neural agents. Our contributions are twofold: first, we show that the learning algorithm used to imitate is crucial: supervised learning tends to produce more average languages, while reinforcement learning introduces a selection pressure toward more compositional languages. Second, our study reveals that compositional languages are easier to imitate, which may induce the pressure toward compositional languages in RL imitation settings.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-acl.787</identifier>
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%0 Conference Proceedings
%T On the Correspondence between Compositionality and Imitation in Emergent Neural Communication
%A Cheng, Emily
%A Rita, Mathieu
%A Poibeau, Thierry
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F cheng-etal-2023-correspondence
%X Compositionality is a hallmark of human language that not only enables linguistic generalization, but also potentially facilitates acquisition. When simulating language emergence with neural networks, compositionality has been shown to improve communication performance; however, its impact on imitation learning has yet to be investigated. Our work explores the link between compositionality and imitation in a Lewis game played by deep neural agents. Our contributions are twofold: first, we show that the learning algorithm used to imitate is crucial: supervised learning tends to produce more average languages, while reinforcement learning introduces a selection pressure toward more compositional languages. Second, our study reveals that compositional languages are easier to imitate, which may induce the pressure toward compositional languages in RL imitation settings.
%R 10.18653/v1/2023.findings-acl.787
%U https://aclanthology.org/2023.findings-acl.787
%U https://doi.org/10.18653/v1/2023.findings-acl.787
%P 12432-12447

Markdown (Informal)

[On the Correspondence between Compositionality and Imitation in Emergent Neural Communication](https://aclanthology.org/2023.findings-acl.787) (Cheng et al., Findings 2023)

• On the Correspondence between Compositionality and Imitation in Emergent Neural Communication (Cheng et al., Findings 2023)

ACL

• Emily Cheng, Mathieu Rita, and Thierry Poibeau. 2023. On the Correspondence between Compositionality and Imitation in Emergent Neural Communication. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12432–12447, Toronto, Canada. Association for Computational Linguistics.