Conversation Disentanglement

Conversation disentanglement aims to separate overlapping conversations into distinct threads, improving the analysis and understanding of multi-party dialogues. Current research focuses on developing models that leverage various features, including discourse structure, speaker roles, and contextual information, often employing transformer-based architectures and contrastive learning techniques to effectively group utterances into coherent sessions. This work is significant for advancing natural language processing applications such as dialogue summarization and question answering, as well as providing insights into the dynamics of human communication.