CluCDD:Contrastive Dialogue Disentanglement via Clustering

• URL: http://arxiv.org/abs/2302.08146v1
• Date: Thu, 16 Feb 2023 08:47:51 GMT
• Title: CluCDD:Contrastive Dialogue Disentanglement via Clustering
• Authors: Jingsheng Gao, Zeyu Li, Suncheng Xiang, Ting Liu, Yuzhuo Fu
• Abstract summary: A huge number of multi-participant dialogues happen online every day. Dialogue disentanglement aims at separating an entangled dialogue into detached sessions. We propose a model named CluCDD, which aggregates utterances by contrastive learning.
• Score: 18.06976502939079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A huge number of multi-participant dialogues happen online every day, which leads to difficulty in understanding the nature of dialogue dynamics for both humans and machines. Dialogue disentanglement aims at separating an entangled dialogue into detached sessions, thus increasing the readability of long disordered dialogue. Previous studies mainly focus on message-pair classification and clustering in two-step methods, which cannot guarantee the whole clustering performance in a dialogue. To address this challenge, we propose a simple yet effective model named CluCDD, which aggregates utterances by contrastive learning. More specifically, our model pulls utterances in the same session together and pushes away utterances in different ones. Then a clustering method is adopted to generate predicted clustering labels. Comprehensive experiments conducted on the Movie Dialogue dataset and IRC dataset demonstrate that our model achieves a new state-of-the-art result.

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