Abstract
Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i.e., as independent tasks. In this article, we (1) explain how both tasks are related at their core by a common quantity, and (2) propose a simple and intuitive joint probabilistic model that addresses both via joint computation but task-specific application of that quantity. In our experiments with two TREC datasets, our joint model substantially outperforms state-of-the-art systems in both tasks.- Anthology ID:
- Q16-1009
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 4
- Month:
- Year:
- 2016
- Address:
- Cambridge, MA
- Editors:
- Lillian Lee, Mark Johnson, Kristina Toutanova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 113–125
- Language:
- URL:
- https://aclanthology.org/Q16-1009
- DOI:
- 10.1162/tacl_a_00087
- Cite (ACL):
- Md Arafat Sultan, Vittorio Castelli, and Radu Florian. 2016. A Joint Model for Answer Sentence Ranking and Answer Extraction. Transactions of the Association for Computational Linguistics, 4:113–125.
- Cite (Informal):
- A Joint Model for Answer Sentence Ranking and Answer Extraction (Sultan et al., TACL 2016)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/Q16-1009.pdf