Abstract
We present a grounded neural dialogue model that successfully collaborates with people in a partially-observable reference game. We focus on a setting where two agents each observe an overlapping part of a world context and need to identify and agree on some object they share. Therefore, the agents should pool their information and communicate pragmatically to solve the task. Our dialogue agent accurately grounds referents from the partner’s utterances using a structured reference resolver, conditions on these referents using a recurrent memory, and uses a pragmatic generation procedure to ensure the partner can resolve the references the agent produces. We evaluate on the OneCommon spatial grounding dialogue task (Udagawa and Aizawa 2019), involving a number of dots arranged on a board with continuously varying positions, sizes, and shades. Our agent substantially outperforms the previous state of the art for the task, obtaining a 20% relative improvement in successful task completion in self-play evaluations and a 50% relative improvement in success in human evaluations.- Anthology ID:
- 2021.emnlp-main.163
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2130–2147
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.163
- DOI:
- 10.18653/v1/2021.emnlp-main.163
- Cite (ACL):
- Daniel Fried, Justin Chiu, and Dan Klein. 2021. Reference-Centric Models for Grounded Collaborative Dialogue. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2130–2147, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Reference-Centric Models for Grounded Collaborative Dialogue (Fried et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2021.emnlp-main.163.pdf
- Code
- dpfried/onecommon