Conversation Disentanglement with Bi-Level Contrastive Learning

TL;DR

This paper introduces a bi-level contrastive learning model for conversation disentanglement that improves session grouping by focusing on utterance-to-context relations and works in both supervised and unsupervised settings, achieving state-of-the-art results.

Contribution

The paper presents a novel bi-level contrastive learning approach that enhances conversation disentanglement and operates effectively without requiring extensive labeled data.

Findings

Achieves state-of-the-art performance on multiple datasets.

Works effectively in both supervised and unsupervised settings.

Outperforms existing methods in disentanglement accuracy.

Abstract

Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations. Existing methods have two main drawbacks. First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context relation modeling. Second, huge amount of human annotated data is required for training, which is expensive to obtain in practice. To address these issues, we propose a general disentangle model based on bi-level contrastive learning. It brings closer utterances in the same session while encourages each utterance to be near its clustered session prototypes in the representation space. Unlike existing approaches, our disentangle model works in both supervised setting with labeled data and unsupervised setting when no such data is available. The proposed method achieves new state-of-the-art performance on both settings across several public datasets.