Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition
Pengshan Cai, Hui Wan, Fei Liu, Mo Yu, Hong Yu, Sachindra Joshi
Abstract
We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. Our information acquisition-oriented dialogue system employs a novel adaptation of reinforced self-play so that the system can be transferred to various domains without in-domain dialogue data, and can carry out conversations both informative and attentive to users.- Anthology ID:
- 2022.naacl-main.352
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4781–4796
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.352
- DOI:
- 10.18653/v1/2022.naacl-main.352
- Cite (ACL):
- Pengshan Cai, Hui Wan, Fei Liu, Mo Yu, Hong Yu, and Sachindra Joshi. 2022. Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4781–4796, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition (Cai et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2022.naacl-main.352.pdf
- Code
- ibm/reinforced-dialog-system-for-learning
- Data
- Wizard of Wikipedia