In Task Oriented Dialogue (TOD) system, detecting and inducing new intents are two main challenges to apply the system in the real world. In this paper, we suggest the semantic multi-view model to resolve these two challenges: (1) SBERT for General Embedding (GE), (2) Multi Domain Batch (MDB) for dialogue domain knowledge, and (3) Proxy Gradient Transfer (PGT) for cluster-specialized semantic. MDB feeds diverse dialogue datasets to the model at once to tackle the multi-domain problem by learning the multiple domain knowledge. We introduce a novel method PGT, which employs the Siamese network to fine-tune the model with a clustering method directly.Our model can learn how to cluster dialogue utterances by using PGT. Experimental results demonstrate that our multi-view model with MDB and PGT significantly improves the Open Intent Induction performance compared to baseline systems.

本研究提出一种基于语义多视图模型的任务导向对话系统，通过SBERT进行通用嵌入（GE）， 使用Multi Domain Batch（MDB） 学习多个对话领域的知识，并使用Proxy Gradient Transfer（PGT）进行语义聚类，实现了对实际世界中的新意图检测和诱导的解决方案。实验结果显示，与基准系统相比，我们的多视图模型与MDB和PGT显著提高了Open Intent Induction表现。

对话中多视角零样本开放意图归纳：多领域批处理和代理梯度传递