  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.
