Context-Aware Conversation Thread Detection in Multi-Party Chat
Ming Tan, Dakuo Wang, Yupeng Gao, Haoyu Wang, Saloni Potdar, Xiaoxiao Guo, Shiyu Chang, Mo Yu
Abstract
In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs. In this work, we propose a novel Context-Aware Thread Detection (CATD) model that automatically disentangles these conversation threads. We evaluate our model on four real-world datasets and demonstrate an overall im-provement in thread detection accuracy over state-of-the-art benchmarks.- Anthology ID:
- D19-1682
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6456–6461
- Language:
- URL:
- https://aclanthology.org/D19-1682
- DOI:
- 10.18653/v1/D19-1682
- Cite (ACL):
- Ming Tan, Dakuo Wang, Yupeng Gao, Haoyu Wang, Saloni Potdar, Xiaoxiao Guo, Shiyu Chang, and Mo Yu. 2019. Context-Aware Conversation Thread Detection in Multi-Party Chat. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6456–6461, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Context-Aware Conversation Thread Detection in Multi-Party Chat (Tan et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/D19-1682.pdf