Abstract
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.
Abstract (translated)
在任务定向对话系统(TOD)中,检测和引入新的意图是将其应用于现实世界的两个主要挑战。在本文中,我们建议采用语义多视角模型来解决这两个挑战:(1) SBERT用于一般嵌入(GE),(2) 对话领域知识的多域批量(MDB),(3) 代理梯度转移(PGT)用于簇特定语义。 MDB通过一次性向模型注入多样化的对话数据集来解决多域问题,通过学习多个域知识。我们介绍了一种新的方法PGT,它使用iamese网络微调模型,并通过聚类方法直接进行。我们的模型可以通过使用PGT来学习如何聚类对话 utterances。实验结果表明,与基线系统相比,我们的多视角模型使用MDB和PGT显著提高了开放意图引入性能。
URL
https://arxiv.org/abs/2303.13099