Prompt Pool based Class-Incremental Continual Learning for Dialog State Tracking
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.
Code (1)
Tasks
Continual Learningdialog state trackingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Prompt Customization for Continual Learning
Contemporary continual learning approaches typically select prompts from a pool, which function as supplementary inputs to a pre-trained model. However, this strategy is hindered by the inherent noise of its selection ap…
Continual LearningIncremental LearningOne-stage Prompt-based Continual Learning
Prompt-based Continual Learning (PCL) has gained considerable attention as a promising continual learning solution as it achieves state-of-the-art performance while preventing privacy violation and memory overhead issues…
Continual LearningUniPrompt-CL: Sustainable Continual Learning in Medical AI with Unified Prompt Pools
Modern AI models are typically trained on static datasets, limiting their ability to continuously adapt to rapidly evolving real-world environments. While continual learning (CL) addresses this limitation, most CL method…
Incremental LearningContinual LearningBeyond Prompt Degradation: Prototype-guided Dual-pool Prompting for Incremental Object Detection
Incremental Object Detection (IOD) aims to continuously learn new object categories without forgetting previously learned ones. Recently, prompt-based methods have gained popularity for their replay-free design and param…
General KnowledgeObject DetectionPECTP: Parameter-Efficient Cross-Task Prompts for Incremental Vision Transformer
Incremental Learning (IL) aims to learn deep models on sequential tasks continually, where each new task includes a batch of new classes and deep models have no access to task-ID information at the inference time. Recent…
Incremental Learning