Abstract
As the labeling cost for different modules in task-oriented dialog (ToD) systems is expensive, a major challenge is to train different modules with the least amount of labeled data. Recently, large-scale pre-trained language models (PLMs) have shown promising results for few-shot learning in ToD. This presentation will introduce two recent works to deal with this task from Huawei Noah's Ark Lab Speech & Semantics Lab. The first paper [1] devises a Self-Training approach to utilize the abundant unlabeled dialog data as well as a new text augmentation technique (GradAug) by replacing non-crucial tokens using a masked language model. The second paper [2] proposes Comprehensive Instruction (CINS) that better exploits PLMs with extra task-specific instructions for few-show learning w.r.t. different ToD Downstream tasks. Empirical results on multiple ToD downstream tasks reveal that both approaches consistently and notably outperforms using PLMs with standard finetune.

Paper references:
[1] Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems, EMNLP 2021
[2] CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems, AAAI 2022