Continual learning is crucial for dialog state tracking (DST) in dialog systems, since requirements from users for new functionalities are often encountered. However, most of existing continual learning methods for DST require task identities during testing, which is a severe limit in real-world applications. In this paper, we aim to address continual learning of DST in the class-incremental scenario (namely the task identity is unknown in testing). Inspired by the recently emerging prompt tuning method that performs well on dialog systems, we propose to use the prompt pool method, where we maintain a pool of key-value paired prompts and select prompts from the pool according to the distance between the dialog history and the prompt keys. The proposed method can automatically identify tasks and select appropriate prompts during testing. We conduct experiments on Schema-Guided Dialog dataset (SGD) and another dataset collected from a real-world dialog application. Experiment results show that the prompt pool method achieves much higher joint goal accuracy than the baseline. After combining with a rehearsal buffer, the model performance can be further improved.

翻译：持续学习对于对话系统中的对话状态跟踪（DST）至关重要，因为用户对新功能的需求时常出现。然而，现有大多数面向DST的持续学习方法在测试时需要任务标识，这在实际应用中存在严重局限性。本文旨在解决类增量场景下（即测试时任务标识未知）的DST持续学习问题。受近期在对话系统中表现优异的提示调优方法启发，我们提出采用提示池方法：维护一个键值配对的提示池，根据对话历史与提示键之间的距离从池中选择提示。该方法可在测试时自动识别任务并选择适当提示。我们在Schema-Guided Dialog数据集（SGD）及另一个来自真实对话应用的数据集上进行实验。结果表明，提示池方法在联合目标准确率上显著优于基线方法。结合重放缓冲后，模型性能可进一步提升。