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        <title>Multi-Agent Language Learning: Symbolic Mapping</title>
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    <abstract>The study of emergent communication has long been devoted to coax neural network agents to learn a language sharing similar properties with human language. In this paper, we try to find a ‘natural’ way to help agents learn a compositional and symmetric language in complex settings like dialog games. Inspired by the theory that human language was originated from simple interactions, we hypothesize that language may evolve from simple tasks to difficult tasks. We propose a curriculum learning method called task transfer, and propose a novel architecture called symbolic mapping. We find that task transfer distinctly helps language learning in difficult tasks, and symbolic mapping promotes the effect. Further, we explore vocabulary expansion, and show that with the help of symbolic mapping, agents can easily learn to use new symbols when the environment becomes more complex. All in all, we find that a process from simplicity to complexity can serve as a natural way to help multi-agent language learning, and the proposed symbolic mapping is effective for this process.</abstract>
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%0 Conference Proceedings
%T Multi-Agent Language Learning: Symbolic Mapping
%A Feng, Yicheng
%A Lu, Zongqing
%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 feng-lu-2023-multi
%X The study of emergent communication has long been devoted to coax neural network agents to learn a language sharing similar properties with human language. In this paper, we try to find a ‘natural’ way to help agents learn a compositional and symmetric language in complex settings like dialog games. Inspired by the theory that human language was originated from simple interactions, we hypothesize that language may evolve from simple tasks to difficult tasks. We propose a curriculum learning method called task transfer, and propose a novel architecture called symbolic mapping. We find that task transfer distinctly helps language learning in difficult tasks, and symbolic mapping promotes the effect. Further, we explore vocabulary expansion, and show that with the help of symbolic mapping, agents can easily learn to use new symbols when the environment becomes more complex. All in all, we find that a process from simplicity to complexity can serve as a natural way to help multi-agent language learning, and the proposed symbolic mapping is effective for this process.
%R 10.18653/v1/2023.findings-acl.491
%U https://aclanthology.org/2023.findings-acl.491
%U https://doi.org/10.18653/v1/2023.findings-acl.491
%P 7756-7770

Markdown (Informal)

[Multi-Agent Language Learning: Symbolic Mapping](https://aclanthology.org/2023.findings-acl.491) (Feng & Lu, Findings 2023)

• Multi-Agent Language Learning: Symbolic Mapping (Feng & Lu, Findings 2023)

ACL

• Yicheng Feng and Zongqing Lu. 2023. Multi-Agent Language Learning: Symbolic Mapping. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7756–7770, Toronto, Canada. Association for Computational Linguistics.