@article{Mi_Wang_Li_2022, title={CINS: Comprehensive Instruction for Few-Shot Learning in Task-Oriented Dialog Systems}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/21356}, DOI={10.1609/aaai.v36i10.21356}, abstractNote={As the labeling cost for different modules in task-oriented dialog (ToD) systems is high, a major challenge is to learn different tasks with the least amount of labeled data. Recently, pre-trained language models (PLMs) have shown promising results for few-shot learning in ToD. To better utilize the power of PLMs, this paper proposes Comprehensive Instruction (CINS) that exploits PLMs with extra task-specific instructions. We design a schema (definition, constraint, prompt) of instructions and their customized realizations for three important downstream tasks in ToD, ie. intent classification, dialog state tracking, and natural language generation. A sequence-to-sequence model (T5) is adopted to solve these three tasks in a unified framework. Extensive experiments are conducted on these ToD tasks in realistic few-shot learning scenarios with small validation data. Empirical results demonstrate that the proposed CINS approach consistently improves techniques that finetune PLMs with raw input or short prompt.}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Mi, Fei and Wang, Yasheng and Li, Yitong}, year={2022}, month={Jun.}, pages={11076-11084} }