Papers with multi-agent language learning

1 papers
Multi-Agent Language Learning: Symbolic Mapping (2023.findings-acl)

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Challenge: Recent work has focused on the emergence of language in cooperative tasks where neural network agents learn a communication protocol from scratch to solve problems together.
Approach: They propose a task transfer method and symbolic mapping architecture to help agents learn a compositional and symmetric language in dialog games.
Outcome: The proposed method can help agents learn a compositional and symmetric language in complex settings like dialog games and the proposed architecture promotes vocabulary expansion.

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