Context-Aware Conversation Thread Detection in Multi-Party Chat

EMNLP

Cite Paper

Authors

Dakuo Wang
Shiyu Chang
Ming Tan
Yupeng Gao
Haoyu Wang
Saloni Potdar
Xiaoxiao Guo
Mo Yu

Published on

11/07/2019

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.

Please cite our work using the BibTeX below.

@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",
    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",
}