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 graph (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.

通过 TARA 图对用户手册进行表示，可以在各种类型问题上提高对用户手册的机器阅读理解能力，这一方法在应对复杂问题时具有巨大的潜力。