Abstract
Conversational Machine Reading (CMR) requires answering a user’s initial question through multi-turn dialogue interactions based on a given document. Although there exist many effective methods, they largely neglected the alignment between the document and the user-provided information, which significantly affects the intermediate decision-making and subsequent follow-up question generation. To address this issue, we propose a pipeline framework that (1) aligns the aforementioned two sides in an explicit way, (2) makes decisions using a lightweight many-to-many entailment reasoning module, and (3) directly generates follow-up questions based on the document and previously asked questions. Our proposed method achieves state-of-the-art in micro-accuracy and ranks the first place on the public leaderboard of the CMR benchmark dataset ShARC.- Anthology ID:
- 2023.findings-emnlp.866
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13009–13022
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.866
- DOI:
- 10.18653/v1/2023.findings-emnlp.866
- Cite (ACL):
- Yangyang Luo, Shiyu Tian, Caixia Yuan, and Xiaojie Wang. 2023. Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13009–13022, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading (Luo et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.findings-emnlp.866.pdf