Break It Down: A Question Understanding Benchmark

Abstract

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.

Accepted for publication in Transactions of the Association for Computational Linguistics (TACL), 2020. Author's final version

References in corpus (5)

• VQA: Visual Question Answering
• Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
• HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
• Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
• Building a Neural Semantic Parser from a Domain Ontology