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
- Editors:
- Claire Bonial, Tommaso Caselli, Snigdha Chaturvedi, Elizabeth Clark, Ruihong Huang, Mohit Iyyer, Alejandro Jaimes, Heng Ji, Lara J. Martin, Ben Miller, Teruko Mitamura, Nanyun Peng, Joel Tetreault
- Venues:
- NUSE | WNU
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 108–113
- Language:
- URL:
- https://aclanthology.org/2020.nuse-1.13
- DOI:
- 10.18653/v1/2020.nuse-1.13
- Bibkey:
- Cite (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.
- Cite (Informal):
- Frustratingly Hard Evidence Retrieval for QA Over Books (Mou et al., NUSE-WNU 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.nuse-1.13.pdf
- Video:
- http://slideslive.com/38929752
- Data
- NarrativeQA
Export citation
@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)
- Frustratingly Hard Evidence Retrieval for QA Over Books (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.