Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisition

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Abstract

In Emergent Communication (EC) agents learn to communicate with one another, but the protocols that they develop are specialised to their training community. This observation led to research into Zero-Shot Coordination (ZSC) for learning communication strategies that are robust to agents not encountered during training. However, ZSC typically assumes that no prior data is available about the agents that will be encountered in the zero-shot setting. In many cases, this presents an unnecessarily hard problem and rules out communication via preestablished conventions. We propose a novel AI challenge called a Cooperative Language Acquisition Problem (CLAP) in which the ZSC assumptions are relaxed by allowing a 'joiner' agent to learn from a dataset of interactions between agents in a target community. We propose and compare two methods for solving CLAPs: Imitation Learning (IL), and Emergent Communication pretraining and Translation Learning (ECTL), in which an agent is trained in self-play with EC and then learns from the data to translate between the emergent protocol and the target community's protocol.
Original languageEnglish
Title of host publicationAd Hoc Teamwork Workshop at AAAI-24
Publication statusPublished - 26 Feb 2024
EventAd Hoc Teamwork Workshop at AAAI-24 - Vancouver, Canada
Duration: 26 Feb 2024 → …
https://sites.google.com/view/ad-hoc-teamwork/home

Workshop

WorkshopAd Hoc Teamwork Workshop at AAAI-24
Country/TerritoryCanada
CityVancouver
Period26/02/2024 → …
Internet address

Keywords

  • cs.LG
  • cs.CL
  • cs.MA

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