Multi-hop Reading Comprehension through Question Decomposition and Rescoring

Sewon Min, Victor Zhong, Luke Zettlemoyer, Hannaneh Hajishirzi


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.
Anthology ID:
P19-1613
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6097–6109
Language:
URL:
https://aclanthology.org/P19-1613
DOI:
10.18653/v1/P19-1613
Bibkey:
Cite (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.
Cite (Informal):
Multi-hop Reading Comprehension through Question Decomposition and Rescoring (Min et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1613.pdf
Code
 shmsw25/DecompRC +  additional community code
Data
HotpotQA