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        <title>Multi-hop Reading Comprehension through Question Decomposition and Rescoring</title>
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        <namePart type="given">Sewon</namePart>
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    <abstract>Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs. We propose a system for multi-hop RC that decomposes a compositional question into simpler sub-questions that can be answered by off-the-shelf single-hop RC models. Since annotations for such decomposition are expensive, we recast subquestion generation as a span prediction problem and show that our method, trained using only 400 labeled examples, generates sub-questions that are as effective as human-authored sub-questions. We also introduce a new global rescoring approach that considers each decomposition (i.e. the sub-questions and their answers) to select the best final answer, greatly improving overall performance. Our experiments on HotpotQA show that this approach achieves the state-of-the-art results, while providing explainable evidence for its decision making in the form of sub-questions.</abstract>
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%0 Conference Proceedings
%T Multi-hop Reading Comprehension through Question Decomposition and Rescoring
%A Min, Sewon
%A Zhong, Victor
%A Zettlemoyer, Luke
%A Hajishirzi, Hannaneh
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F min-etal-2019-multi
%X Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs. We propose a system for multi-hop RC that decomposes a compositional question into simpler sub-questions that can be answered by off-the-shelf single-hop RC models. Since annotations for such decomposition are expensive, we recast subquestion generation as a span prediction problem and show that our method, trained using only 400 labeled examples, generates sub-questions that are as effective as human-authored sub-questions. We also introduce a new global rescoring approach that considers each decomposition (i.e. the sub-questions and their answers) to select the best final answer, greatly improving overall performance. Our experiments on HotpotQA show that this approach achieves the state-of-the-art results, while providing explainable evidence for its decision making in the form of sub-questions.
%R 10.18653/v1/P19-1613
%U https://aclanthology.org/P19-1613
%U https://doi.org/10.18653/v1/P19-1613
%P 6097-6109

Markdown (Informal)

[Multi-hop Reading Comprehension through Question Decomposition and Rescoring](https://aclanthology.org/P19-1613) (Min et al., ACL 2019)

• Multi-hop Reading Comprehension through Question Decomposition and Rescoring (Min et al., ACL 2019)

ACL

• Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019. Multi-hop Reading Comprehension through Question Decomposition and Rescoring. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6097–6109, Florence, Italy. Association for Computational Linguistics.