Frustratingly Hard Evidence Retrieval for QA Over Books

Xiangyang Mou, Mo Yu, Bingsheng Yao, Chenghao Yang, Xiaoxiao Guo, Saloni Potdar, Hui Su


Abstract
A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth. We formulate BookQA as an open-domain QA task given its similar dependency on evidence retrieval. We further investigate how state-of-the-art open-domain QA approaches can help BookQA. Besides achieving state-of-the-art on the NarrativeQA benchmark, our study also reveals the difficulty of evidence retrieval in books with a wealth of experiments and analysis - which necessitates future effort on novel solutions for evidence retrieval in BookQA.
Anthology ID:
2020.nuse-1.13
Volume:
Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | NUSE | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
108–113
URL:
https://www.aclweb.org/anthology/2020.nuse-1.13
DOI:
Bib Export formats:
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PDF:
https://www.aclweb.org/anthology/2020.nuse-1.13.pdf

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