Intent Segmentation of User Queries Via Discourse Parsing
Vicente Ivan Sanchez Carmona, Yibing Yang, Ziyue Wen, Ruosen Li, Xiaohua Wang, Changjian Hu
Abstract
In this paper, we explore a new approach based on discourse analysis for the task of intent segmentation. Our target texts are user queries from a real-world chatbot. Our results show the feasibility of our approach with an F1-score of 82.97 points, and some advantages and disadvantages compared to two machine learning baselines: BERT and LSTM+CRF.- Anthology ID:
- 2020.iwdp-1.7
- Volume:
- Proceedings of the Second International Workshop of Discourse Processing
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Qun Liu, Deyi Xiong, Shili Ge, Xiaojun Zhang
- Venue:
- iwdp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 38–47
- Language:
- URL:
- https://aclanthology.org/2020.iwdp-1.7
- DOI:
- Cite (ACL):
- Vicente Ivan Sanchez Carmona, Yibing Yang, Ziyue Wen, Ruosen Li, Xiaohua Wang, and Changjian Hu. 2020. Intent Segmentation of User Queries Via Discourse Parsing. In Proceedings of the Second International Workshop of Discourse Processing, pages 38–47, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- Intent Segmentation of User Queries Via Discourse Parsing (Sanchez Carmona et al., iwdp 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.iwdp-1.7.pdf