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        <title>Break It Down: A Question Understanding Benchmark</title>
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    <name type="personal">
        <namePart type="given">Tomer</namePart>
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        <namePart type="given">Mor</namePart>
        <namePart type="family">Geva</namePart>
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    <name type="personal">
        <namePart type="given">Ankit</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00309</identifier>
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%0 Journal Article
%T Break It Down: A Question Understanding Benchmark
%A Wolfson, Tomer
%A Geva, Mor
%A Gupta, Ankit
%A Gardner, Matt
%A Goldberg, Yoav
%A Deutch, Daniel
%A Berant, Jonathan
%J Transactions of the Association for Computational Linguistics
%D 2020
%V 8
%I MIT Press
%C Cambridge, MA
%F wolfson-etal-2020-break
%X 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.
%R 10.1162/tacl_a_00309
%U https://aclanthology.org/2020.tacl-1.13
%U https://doi.org/10.1162/tacl_a_00309
%P 183-198

Markdown (Informal)

[Break It Down: A Question Understanding Benchmark](https://aclanthology.org/2020.tacl-1.13) (Wolfson et al., TACL 2020)

• Break It Down: A Question Understanding Benchmark (Wolfson et al., TACL 2020)

ACL

• Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, and Jonathan Berant. 2020. Break It Down: A Question Understanding Benchmark. Transactions of the Association for Computational Linguistics, 8:183–198.