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CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems
[article]
2022
arXiv
pre-print
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,
arXiv:2109.04645v4
fatcat:ak2l7tjrsrck5lnthbd7in6kjq