Abstract
Previous work on question-answering systems mainly focuses on answering individual questions, assuming they are independent and devoid of context. Instead, we investigate sequential question answering, asking multiple related questions. We present QBLink, a new dataset of fully human-authored questions. We extend existing strong question answering frameworks to include previous questions to improve the overall question-answering accuracy in open-domain question answering. The dataset is publicly available at http://sequential.qanta.org.- Anthology ID:
- D18-1134
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1077–1083
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/D18-1134/
- DOI:
- 10.18653/v1/D18-1134
- Cite (ACL):
- Ahmed Elgohary, Chen Zhao, and Jordan Boyd-Graber. 2018. A dataset and baselines for sequential open-domain question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1077–1083, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- A dataset and baselines for sequential open-domain question answering (Elgohary et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/D18-1134.pdf