Huge amounts of textual conversations occur online everyday, where multiple conversations take place concurrently. Interleaved conversations lead to difficulties in not only following the ongoing discussions but also extracting relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations. However existing disentanglement methods rely mostly on hand-crafted features that are dataset specific, which hinders generalization and adaptability. In this work, we propose an end-to-end online framework for conversation disentanglement that avoids time-consuming domain-specific feature engineering. We design a novel way to embed the whole utterance that comprises timestamp, speaker and message text, and propose a custom attention mechanism that models disentanglement as a pointing problem while effectively capturing inter-utterance interactions in an end-to-end fashion. We also introduce a joint-learning objective to better capture contextual information. Our experiments on the Ubuntu IRC dataset show that our method achieves state-of-the-art performance in both link and conversation prediction tasks.

@inproceedings{Tao-emnlp-20, abstract = {Huge amounts of textual conversations occur online everyday, where multiple conversations take place concurrently. Interleaved conversations lead to difficulties in not only following the ongoing discussions but also extracting relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations. However existing disentanglement methods rely mostly on hand-crafted features that are dataset specific, which hinders generalization and adaptability. In this work, we propose an end-to-end online framework for conversation disentanglement that avoids time-consuming domain-specific feature engineering. We design a novel way to embed the whole utterance that comprises timestamp, speaker and message text, and propose a custom attention mechanism that models disentanglement as a pointing problem while effectively capturing inter-utterance interactions in an end-to-end fashion. We also introduce a joint-learning objective to better capture contextual information. Our experiments on the Ubuntu IRC dataset show that our method achieves state-of-the-art performance in both link and conversation prediction tasks.}, address = {Punta Cana, Dominican Republic}, author = {Tao Yu and Shafiq Joty}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing}, numpages = {9}, pages = {6321–-6330}, publisher = {ACL}, series = {EMNLP'20}, title = {Online Conversation Disentanglement with Pointer Networks}, url = {https://www.aclweb.org/anthology/2020.emnlp-main.512/}, year = {2020} }