@inproceedings{liang-etal-2023-knowing,
    title = "Knowing-how {\&} Knowing-that: A New Task for Machine Comprehension of User Manuals",
    author = "Liang, Hongru  and
      Liu, Jia  and
      Du, Weihong  and
      Jin, Dingnan  and
      Lei, Wenqiang  and
      Wen, Zujie  and
      Lv, Jiancheng",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.671",
    doi = "10.18653/v1/2023.findings-acl.671",
    pages = "10550--10564",
    abstract = "The machine reading comprehension (MRC) of user manuals has huge potential in customer service. However, current methods have trouble answering complex questions. Therefore, we introduce the knowing-how {\&} knowing-that task that requires the model to answer factoid-style, procedure-style, and inconsistent questions about user manuals. We resolve this task by jointly representing the sTeps and fActs in a gRAh (TARA), which supports a unified inference of various questions. Towards a systematical benchmarking study, we design a heuristic method to automatically parse user manuals into TARAs and build an annotated dataset to test the model{'}s ability in answering real-world questions. Empirical results demonstrate that representing user manuals as TARAs is a desired solution for the MRC of user manuals. An in-depth investigation of TARA further sheds light on the issues and broader impacts of future representations of user manuals. We hope our work can move the MRC of user manuals to a more complex and realistic stage.",
}

The machine reading comprehension (MRC) of user manuals has huge potential in customer service. However, current methods have trouble answering complex questions. Therefore, we introduce the knowing-how {\&} knowing-that task that requires the model to answer factoid-style, procedure-style, and inconsistent questions about user manuals. We resolve this task by jointly representing the sTeps and fActs in a gRAh (TARA), which supports a unified inference of various questions. Towards a systematical benchmarking study, we design a heuristic method to automatically parse user manuals into TARAs and build an annotated dataset to test the model{'}s ability in answering real-world questions. Empirical results demonstrate that representing user manuals as TARAs is a desired solution for the MRC of user manuals. An in-depth investigation of TARA further sheds light on the issues and broader impacts of future representations of user manuals. We hope our work can move the MRC of user manuals to a more complex and realistic stage.
Liang, Hongru Liu, Jia Du, Weihong Jin, Dingnan Lei, Wenqiang Wen, Zujie Lv, Jiancheng
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals