Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading

• URL: http://arxiv.org/abs/2310.13409v1
• Date: Fri, 20 Oct 2023 10:27:24 GMT
• Title: Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading
• Authors: Yangyang Luo, Shiyu Tian, Caixia Yuan, Xiaojie Wang
• Abstract summary: Conversational Machine Reading (CMR) requires answering a user's initial question through multi-turn dialogue interactions based on a given document. 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.
• Score: 8.910847114561191
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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