Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction

Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata, Atsushi Otsuka, Itsumi Saito, Hisako Asano, Junji Tomita


Abstract
Question answering (QA) using textual sources for purposes such as reading comprehension (RC) has attracted much attention. This study focuses on the task of explainable multi-hop QA, which requires the system to return the answer with evidence sentences by reasoning and gathering disjoint pieces of the reference texts. It proposes the Query Focused Extractor (QFE) model for evidence extraction and uses multi-task learning with the QA model. QFE is inspired by extractive summarization models; compared with the existing method, which extracts each evidence sentence independently, it sequentially extracts evidence sentences by using an RNN with an attention mechanism on the question sentence. It enables QFE to consider the dependency among the evidence sentences and cover important information in the question sentence. Experimental results show that QFE with a simple RC baseline model achieves a state-of-the-art evidence extraction score on HotpotQA. Although designed for RC, it also achieves a state-of-the-art evidence extraction score on FEVER, which is a recognizing textual entailment task on a large textual database.
Anthology ID:
P19-1225
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:
2335–2345
Language:
URL:
https://aclanthology.org/P19-1225
DOI:
10.18653/v1/P19-1225
Bibkey:
Cite (ACL):
Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata, Atsushi Otsuka, Itsumi Saito, Hisako Asano, and Junji Tomita. 2019. Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2335–2345, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction (Nishida et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1225.pdf
Supplementary:
 P19-1225.Supplementary.pdf
Data
HotpotQAMS MARCOTriviaQA