  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.
