@inproceedings{ba3b566d383448c9babd0655a78fdd32,
title = "MUIDIAL: Improving Dialogue Disentanglement with Intent-Based Mutual Learning",
abstract = "The main goal of dialogue disentanglement is to separate the mixed utterances from a chat slice into independent dialogues. Existing models often utilize either an utterance-to-utterance (U2U) prediction to determine whether two utterances that have the “reply-to” relationship belong to one dialogue, or an utterance-to-thread (U2T) prediction to determine which dialogue-thread a given utterance should belong to. Inspired by mutual leaning, we propose MUIDIAL, a novel dialogue disentanglement model, to exploit the intent of each utterance and feed the intent to a mutual learning U2U-U2T disentanglement model. Experimental results and in-depth analysis on several benchmark datasets demonstrate the effectiveness and generalizability of our approach.",
author = "Ziyou Jiang and Lin Shi and Celia Chen and Fangwen Mu and Yumin Zhang and Qing Wang",
note = "Publisher Copyright: {\textcopyright} 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.; 31st International Joint Conference on Artificial Intelligence, IJCAI 2022 ; Conference date: 23-07-2022 Through 29-07-2022",
year = "2022",
doi = "10.24963/ijcai.2022/578",
language = "英语",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",
pages = "4164--4170",
editor = "\{De Raedt\}, Luc and \{De Raedt\}, Luc",
booktitle = "Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022",
address = "美国",
}