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.

翻译：在任务型对话系统中，检测和归纳新意图是将系统应用于现实世界的两大挑战。本文提出语义多视角模型来解决这两个挑战：（1）用于通用嵌入的SBERT，（2）用于对话领域知识的多领域批量，以及（3）用于聚类专业化语义的代理梯度迁移。多领域批量同时将多样化的对话数据集输入模型，通过学习多领域知识来解决多领域问题。我们引入了一种新颖的代理梯度迁移方法，该方法采用孪生网络直接通过聚类方法对模型进行微调。我们的模型能够利用代理梯度迁移学习如何对对话语句进行聚类。实验结果表明，与基线系统相比，我们结合多领域批量与代理梯度迁移的多视角模型显著提升了开放意图归纳的性能。