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Intent recognition is critical for task-oriented dialogue systems. However,for emerging domains and new services, it is difficult to accurately identifythe key intent of a conversation due to time-consuming data annotation andcomparatively poor model transferability. Therefore, the automatic induction ofdialogue intention is very important for intelligent dialogue systems. Thispaper presents our solution to Track 2 of Intent Induction from Conversationsfor Task-Oriented Dialogue at the Eleventh Dialogue System Technology Challenge(DSTC11). The essence of intention clustering lies in distinguishing therepresentation of different dialogue utterances. The key to automatic intentioninduction is that, for any given set of new data, the sentence representationobtained by the model can be well distinguished from different labels.Therefore, we propose a multi-stage coarse-to-fine contrastive learning modeltraining scheme including unsupervised contrastive learning pre-training,supervised contrastive learning pre-training, and fine-tuning with jointcontrastive learning and clustering to obtain a better dialogue utterancerepresentation model for the clustering task. In the released DSTC11 Track 2evaluation results, our proposed system ranked first on both of the twosubtasks of this Track.

Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction