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
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6456–6461
Language:
URL:
https://www.aclweb.org/anthology/D19-1682
DOI:
10.18653/v1/D19-1682
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/D19-1682.pdf
Attachment:
 D19-1682.Attachment.zip