@inproceedings{mou-etal-2020-frustratingly,
title = "Frustratingly Hard Evidence Retrieval for {QA} Over Books",
author = "Mou, Xiangyang and
Yu, Mo and
Yao, Bingsheng and
Yang, Chenghao and
Guo, Xiaoxiao and
Potdar, Saloni and
Su, Hui",
editor = "Bonial, Claire and
Caselli, Tommaso and
Chaturvedi, Snigdha and
Clark, Elizabeth and
Huang, Ruihong and
Iyyer, Mohit and
Jaimes, Alejandro and
Ji, Heng and
Martin, Lara J. and
Miller, Ben and
Mitamura, Teruko and
Peng, Nanyun and
Tetreault, Joel",
booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nuse-1.13/",
doi = "10.18653/v1/2020.nuse-1.13",
pages = "108--113",
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."
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%0 Conference Proceedings
%T Frustratingly Hard Evidence Retrieval for QA Over Books
%A Mou, Xiangyang
%A Yu, Mo
%A Yao, Bingsheng
%A Yang, Chenghao
%A Guo, Xiaoxiao
%A Potdar, Saloni
%A Su, Hui
%Y Bonial, Claire
%Y Caselli, Tommaso
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Huang, Ruihong
%Y Iyyer, Mohit
%Y Jaimes, Alejandro
%Y Ji, Heng
%Y Martin, Lara J.
%Y Miller, Ben
%Y Mitamura, Teruko
%Y Peng, Nanyun
%Y Tetreault, Joel
%S Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F mou-etal-2020-frustratingly
%X 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.
%R 10.18653/v1/2020.nuse-1.13
%U https://aclanthology.org/2020.nuse-1.13/
%U https://doi.org/10.18653/v1/2020.nuse-1.13
%P 108-113
Markdown (Informal)
[Frustratingly Hard Evidence Retrieval for QA Over Books](https://aclanthology.org/2020.nuse-1.13/) (Mou et al., NUSE-WNU 2020)
ACL
- Xiangyang Mou, Mo Yu, Bingsheng Yao, Chenghao Yang, Xiaoxiao Guo, Saloni Potdar, and Hui Su. 2020. Frustratingly Hard Evidence Retrieval for QA Over Books. In Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events, pages 108–113, Online. Association for Computational Linguistics.