Discovering new user intents based on existing intents from constantly incoming unlabeled data is an important task in many intelligent systems deployed in the real world (e.g., dialogue systems). Since data with new intents are completely unlabeled, most current approaches employ clustering methods to generate pseudo labels to train their models. However, due to intent gaps between existing and new intents, pseudo labels generated by these models are noisy, and prior knowledge from existing intents is not fully utilized. To mitigate these issues, we propose a robust pseudo label training and source domain joint-training network to refine the noisy pseudo labels and make full use of prior knowledge. Experimental results on three intent detection datasets show that our model is more effective and robust than state-of-the-art methods. The code and data are released at https://github.com/Lackel/PTJN.

中文翻译：

---

通过鲁棒的伪标签训练和源域联合训练来发现新用户意图

根据不断传入的未标记数据中的现有意图发现新的用户意图是现实世界中部署的许多智能系统（例如对话系统）中的一项重要任务。由于具有新意图的数据完全没有标签，因此当前大多数方法都采用聚类方法来生成伪标签来训练模型。然而，由于现有意图和新意图之间的意图差距，这些模型生成的伪标签是有噪声的，并且现有意图的先验知识没有得到充分利用。为了缓解这些问题，我们提出了一种鲁棒的伪标签训练和源域联合训练网络，以细化噪声伪标签并充分利用先验知识。三个意图检测数据集的实验结果表明，我们的模型比最先进的方法更有效、更稳健。