  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.
