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        <title>Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading</title>
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        <namePart type="given">Yangyang</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.866</identifier>
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%0 Conference Proceedings
%T Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading
%A Luo, Yangyang
%A Tian, Shiyu
%A Yuan, Caixia
%A Wang, Xiaojie
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F luo-etal-2023-explicit
%X 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.
%R 10.18653/v1/2023.findings-emnlp.866
%U https://aclanthology.org/2023.findings-emnlp.866
%U https://doi.org/10.18653/v1/2023.findings-emnlp.866
%P 13009-13022

Markdown (Informal)

[Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading](https://aclanthology.org/2023.findings-emnlp.866) (Luo et al., Findings 2023)

• Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading (Luo et al., Findings 2023)

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.