ASQ: Automatically Generating Question-Answer Pairs using AMRs
This work addresses the need for accessible semantic role labeling without linguistic expertise by enabling automatic dataset creation in various domains, though it is incremental as it builds on existing AMR parsers and QAMR datasets.
The researchers tackled the problem of automatically generating question-answer pairs from sentences using Abstract Meaning Representation (AMR), resulting in a tool called ASQ that produces natural and valid outputs with good coverage of content, as shown in qualitative evaluations on AMR 2.0 data and QAMR dataset sentences.
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.