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.

翻译：然而,对于新兴领域和新服务而言,由于数据说明耗时且模式可转让性较差,因此难以准确确定对话的关键意图,因此,对话的自动启动意图对于智能对话系统非常重要。本文件介绍了我们在第十一次对话系统技术挑战(DSTC11)中从以任务为方向的对话中引入2项内容的解决办法,意图组合的实质在于区分不同对话言论的表述。对于任何一套特定的新数据而言,自动引入意图的关键是,该模型获得的句子代表可以与不同的标签区分。因此,我们提议了一个多阶段的从粗到软的对比学习模式培训计划,包括非超水平的对比学习前培训、监督的对比学习前培训,以及与联合对比学习和组合进行微调,以便为组合任务获得更好的对话表达模式。在所公布的DSTC11轨道2评价结果中,我们提议的系统在这条轨道的两个子项目中排名第一。</s>