  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.
