Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning

• URL: http://arxiv.org/abs/2005.07064v1
• Date: Thu, 14 May 2020 15:32:23 GMT
• Title: Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning
• Authors: Angeliki Lazaridou, Anna Potapenko, Olivier Tieleman
• Abstract summary: We present a method for combining multi-agent communication and traditional data-driven approaches to natural language learning. Our starting point is a language model that has been trained on generic, not task-specific language data. We then place this model in a multi-agent self-play environment that generates task-specific rewards used to adapt or modulate the model.
• Score: 16.776753238108036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a method for combining multi-agent communication and traditional data-driven approaches to natural language learning, with an end goal of teaching agents to communicate with humans in natural language. Our starting point is a language model that has been trained on generic, not task-specific language data. We then place this model in a multi-agent self-play environment that generates task-specific rewards used to adapt or modulate the model, turning it into a task-conditional language model. We introduce a new way for combining the two types of learning based on the idea of reranking language model samples, and show that this method outperforms others in communicating with humans in a visual referential communication task. Finally, we present a taxonomy of different types of language drift that can occur alongside a set of measures to detect them.

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