Dual-Teacher and Dual-Prompt Pool for Few-Shot Dialog State Tracking

Dialog state tracking (DST) is an essential component of task-oriented dialog (ToD) systems. Existing few-shot DST models face challenges in effectively leveraging semantically related information and exhibit limited adaptability to dialog scenarios. To address these challenges, the dual-teacher and dual-prompt pool model for few-shot DST (DDP-DST) is proposed. Specifically, by enhancing key semantic information and syntactic structure, dual teacher models are constructed to generate pseudolabels from complementary perspectives. Self-training is employed to further improve state value generation. Besides, considering the intrinsic symmetry between slot and state value generation tasks, a dual-prompt fine-tuning strategy is designed. A dynamic prompt pool is constructed to adaptively generate prompts. Reconstruction errors (REs) are fed back into the DDP-DST model, leading to improved accuracy in DST. Experimental results on the MultiWOZ 2.1 dataset demonstrate that DDP-DST outperforms baseline models such as SM2-3b, DS2, and SVAG with average improvements of 4.3%, 2.4%, and 2.0% in the metric of joint goal accuracy (JGA). Notably, with fewer than 1 billion parameters, DDP-DST achieves competitive performance in few-shot settings, even outperforming models with up to 10 billion parameters.