On the Correspondence between Compositionality and Imitation in Emergent Neural Communication

• URL: http://arxiv.org/abs/2305.12941v1
• Date: Mon, 22 May 2023 11:41:29 GMT
• Title: On the Correspondence between Compositionality and Imitation in Emergent Neural Communication
• Authors: Emily Cheng, Mathieu Rita, Thierry Poibeau
• Abstract summary: Our work explores the link between compositionality and imitation in a Lewis game played by deep neural agents. supervised learning tends to produce more average languages, while reinforcement learning introduces a selection pressure toward more compositional languages.
• Score: 1.4610038284393165
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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