@inproceedings{tan-etal-2019-context,
title = "Context-Aware Conversation Thread Detection in Multi-Party Chat",
author = "Tan, Ming and
Wang, Dakuo and
Gao, Yupeng and
Wang, Haoyu and
Potdar, Saloni and
Guo, Xiaoxiao and
Chang, Shiyu and
Yu, Mo",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1682/",
doi = "10.18653/v1/D19-1682",
pages = "6456--6461",
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."
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%0 Conference Proceedings
%T Context-Aware Conversation Thread Detection in Multi-Party Chat
%A Tan, Ming
%A Wang, Dakuo
%A Gao, Yupeng
%A Wang, Haoyu
%A Potdar, Saloni
%A Guo, Xiaoxiao
%A Chang, Shiyu
%A Yu, Mo
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F tan-etal-2019-context
%X 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.
%R 10.18653/v1/D19-1682
%U https://aclanthology.org/D19-1682/
%U https://doi.org/10.18653/v1/D19-1682
%P 6456-6461
Markdown (Informal)
[Context-Aware Conversation Thread Detection in Multi-Party Chat](https://aclanthology.org/D19-1682/) (Tan et al., EMNLP-IJCNLP 2019)
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.