@inproceedings{luo-etal-2023-explicit,
    title = "Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading",
    author = "Luo, Yangyang  and
      Tian, Shiyu  and
      Yuan, Caixia  and
      Wang, Xiaojie",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.findings-emnlp.866/",
    doi = "10.18653/v1/2023.findings-emnlp.866",
    pages = "13009--13022",
    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 $\textit{document}$ and the $\textit{user-provided information}$, 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."
}