ASQ: Automatically Generating Question-Answer Pairs using AMRs

arXiv (Cornell University)(2021)

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In this work, we introduce ASQ, a tool to automatically mine questions and answers from a sentence, using its Abstract Meaning Representation (AMR). Previous work has made a case for using question-answer pairs to specify predicate-argument structure of a sentence using natural language, which does not require linguistic expertise or training. This has resulted in the creation of datasets such as QA-SRL and QAMR, for both of which, the question-answer pair annotations were crowdsourced. Our approach has the same end-goal, but is automatic, making it faster and cost-effective, without compromising on the quality and validity of the question-answer pairs thus obtained. 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.
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