Abstract
Despite recent progress in conversational question answering, most prior work does not focus on follow-up questions. Practical conversational question answering systems often receive follow-up questions in an ongoing conversation, and it is crucial for a system to be able to determine whether a question is a follow-up question of the current conversation, for more effective answer finding subsequently. In this paper, we introduce a new follow-up question identification task. We propose a three-way attentive pooling network that determines the suitability of a follow-up question by capturing pair-wise interactions between the associated passage, the conversation history, and a candidate follow-up question. It enables the model to capture topic continuity and topic shift while scoring a particular candidate follow-up question. Experiments show that our proposed three-way attentive pooling network outperforms all baseline systems by significant margins.- Anthology ID:
- 2020.acl-main.90
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 959–968
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.90
- DOI:
- 10.18653/v1/2020.acl-main.90
- Cite (ACL):
- Souvik Kundu, Qian Lin, and Hwee Tou Ng. 2020. Learning to Identify Follow-Up Questions in Conversational Question Answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 959–968, Online. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Identify Follow-Up Questions in Conversational Question Answering (Kundu et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.90.pdf