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.- Anthology ID:
- 2020.acl-main.685
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7663–7674
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.685
- DOI:
- 10.18653/v1/2020.acl-main.685
- Cite (ACL):
- Angeliki Lazaridou, Anna Potapenko, and Olivier Tieleman. 2020. Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7663–7674, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning (Lazaridou et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.685.pdf