  We introduce ASQ, a tool to automatically mine questions and answers from a
sentence using the Abstract Meaning Representation (AMR). Previous work has
used question-answer pairs to specify the predicate-argument structure of a
sentence using natural language, which does not require linguistic expertise or
training, and created datasets such as QA-SRL and QAMR, for which the
question-answer pair annotations were crowdsourced. Our goal is to build a tool
(ASQ) that maps from the traditional meaning representation AMR to a
question-answer meaning representation (QMR). This enables construction of QMR
datasets automatically in various domains using existing high-quality AMR
parsers, and provides an automatic mapping AMR to QMR for ease of understanding
by non-experts. A qualitative evaluation of the output generated by ASQ from
the AMR 2.0 data shows that the question-answer pairs are natural and valid,
and demonstrate good coverage of the content. We run ASQ on the sentences from
the QAMR dataset, to observe that the semantic roles in QAMR are also captured
by ASQ. We intend to make this tool and the results publicly available for
others to use and build upon.
