As labeling cost for different modules in task-oriented dialog (ToD) systems is high, a major challenge in practice is to learn different tasks with the least amount of labeled data. Recently, prompting methods over 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, i.e. 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 prompts.

本文提出Comprehensive Instruction (CINS) 方法，利用预训练语言模型(PLMs)的额外任务特定指令，用序列到序列模型(T5)解决ToD中的三个任务，并在少量验证数据的现实少样本学习场景中进行了实验。实证结果表明，相比于使用原始输入或短提示调整PLMs的技术，该方法不断改进，并显著提高了ToD任务的性能。

CINS: 任务导向对话系统中的Few-shot学习综合指南