  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.
