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.

翻译：我们引入了ASQ,这是使用“抽象含义说明”自动解答问答的工具。先前的工作使用问答对口来具体说明使用自然语言(不需要语言专门知识或培训)的句子的上游参数结构,并创建了诸如QA-SRL和QAMR等数据集,问答对口说明来自多方来源。我们的目标是建立一个工具(ASQ),从传统含义代表AMR到问答含义说明(QMR)的地图。这样就可以利用现有高质量AMR对口器在多个领域自动构建QMR数据集,并提供自动测绘AMR到QMR,以便于非专家理解。对ASQ从AMR2.0数据产生的输出进行质量评估,表明问答对口是自然而有效的,并显示内容的涵盖范围良好。我们对QMR数据集的句子进行ASQQQ,以观察QAMR中语义作用也由ASQQQQQQMR公开掌握,供公众使用。我们打算建立这一工具并获取其他结果。