Recently, low-resource dialogue state tracking (DST) has received increasing attention. First obtaining state values then based on values to generate slot types has made great progress in this task. However, obtaining state values is still an under-studied problem. Existing extraction-based approaches cannot capture values that require the understanding of context and are not generalizable either. To address these issues, we propose a novel State VAlue Generation based framework (SVAG), decomposing DST into state value generation and domain slot generation. Specifically, we propose to generate state values and use self-training to further improve state value generation. Moreover, we design an estimator aiming at detecting incomplete generation and incorrect generation for pseudo-labeled data selection during self-training. Experimental results on the MultiWOZ 2.1 dataset show that our method which has only less than 1 billion parameters achieves state-of-the-art performance under the data ratio settings of 5%, 10%, and 25% when limited to models under 100 billion parameters. Compared to models with more than 100 billion parameters, SVAG still reaches competitive results.

翻译：近年来，低资源对话状态追踪（DST）受到越来越多关注。先获取状态值，再基于值生成槽类型的方法在该任务中取得了显著进展。然而，状态值获取仍是一个研究不足的问题。现有基于抽取的方法既无法捕捉需要理解上下文的值，也不具备良好的泛化能力。为解决这些问题，我们提出了一种新颖的基于状态值生成的框架（SVAG），将DST分解为状态值生成和领域槽生成两个子任务。具体而言，我们提出生成状态值，并利用自训练进一步提升状态值生成性能。此外，我们设计了一个评估器，用于在自训练过程中检测伪标注数据的不完整生成和错误生成。在MultiWOZ 2.1数据集上的实验结果表明，在限制模型参数不超过1000亿的条件下，我们仅凭不足10亿参数的方法在5%、10%和25%数据比例设置下均达到了最优性能。与超过1000亿参数的模型相比，SVAG仍取得了具有竞争力的结果。