Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction
This work addresses the challenge of intent recognition for emerging domains in task-oriented dialogue systems, but it is incremental as it builds on existing contrastive learning and clustering techniques.
The paper tackled the problem of automatically inducing intents from conversations in task-oriented dialogue systems, where data annotation is costly and model transferability is poor, by proposing a multi-stage coarse-to-fine contrastive learning model that achieved first place in both subtasks of the DSTC11 Track 2 evaluation.
Intent recognition is critical for task-oriented dialogue systems. However, for emerging domains and new services, it is difficult to accurately identify the key intent of a conversation due to time-consuming data annotation and comparatively poor model transferability. Therefore, the automatic induction of dialogue intention is very important for intelligent dialogue systems. This paper presents our solution to Track 2 of Intent Induction from Conversations for Task-Oriented Dialogue at the Eleventh Dialogue System Technology Challenge (DSTC11). The essence of intention clustering lies in distinguishing the representation of different dialogue utterances. The key to automatic intention induction is that, for any given set of new data, the sentence representation obtained by the model can be well distinguished from different labels. Therefore, we propose a multi-stage coarse-to-fine contrastive learning model training scheme including unsupervised contrastive learning pre-training, supervised contrastive learning pre-training, and fine-tuning with joint contrastive learning and clustering to obtain a better dialogue utterance representation model for the clustering task. In the released DSTC11 Track 2 evaluation results, our proposed system ranked first on both of the two subtasks of this Track.