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.

翻译：对话解缠旨在将话语分组为独立的会话，这是处理多方对话的一项基础任务。现有方法存在两个主要缺陷：首先，它们过度强调话语间的成对关系，而对话语与上下文的关系建模关注不足；其次，训练需要大量人工标注数据，这在实践中获取成本高昂。为解决这些问题，我们提出了一种基于双层级对比学习的通用解缠模型。该模型使同一会话内的话语在表示空间中相互靠近，同时鼓励每个话语接近其所属的聚类会话原型。与现有方法不同，我们的解缠模型既可在有标注数据的监督设置下工作，也可在无标注数据的无监督设置下运行。所提方法在多个公开数据集上的两种设置中均取得了新的最先进性能。