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.

提出了一种基于特征值生成的新颖对话状态追踪框架 (SVAG)，将 DST 分解为状态值的生成和领域槽的生成，并使用自我训练来进一步改善状态值的生成。实验证明，在模型参数限制在 1000 亿以下的情况下，该方法在数据比例为 5％、10％和 25％的 MultiWOZ 2.1 数据集上取得了最新的性能，与拥有 1000 亿以上的参数的模型相比，SVAG 仍然取得了有竞争力的结果。