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 three real-world datasets and demonstrate an overall improvement in thread detection accuracy over state-of-the-art benchmarks.
CITATION STYLE
Tan, M., Wang, D., Gao, Y., Wang, H., Potdar, S., Guo, X., … Yu, M. (2019). Context-aware conversation thread detection in multi-party chat. In EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference (pp. 6456–6461). Association for Computational Linguistics. https://doi.org/10.18653/v1/d19-1682
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