Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through natural language, that are necessary for answering a question. We develop a crowdsourcing pipeline, showing that quality QDMRs can be annotated at scale, and release the Break dataset, containing over 83K pairs of questions and their QDMRs. We demonstrate the utility of QDMR by showing that (a) it can be used to improve open-domain question answering on the HotpotQA dataset, (b) it can be deterministically converted to a pseudo-SQL formal language, which can alleviate annotation in semantic parsing applications. Last, we use Break to train a sequence-to-sequence model with copying that parses questions into QDMR structures, and show that it substantially outperforms several natural baselines.

本研究提出了问题分解意义表示（QDMR）来解决自然语言问题分解步骤的计算问题，并通过众包管道进行了标注。利用QDMR改进了HotpotQA数据集的开放领域问题回答，并将其转换成伪SQL语言，在语义分析应用中减轻注释难度。通过Break训练序列到序列模型，并表明其性能优于几种自然基线的算法。

问答理解基准测试