Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation. (arXiv:2205.09661v2 [cs.CL] UPDATED)

In task-oriented dialogue systems, response generation from meaning
representations (MRs) often suffers from limited training examples, due to the
high cost of annotating MR-to-Text pairs. Previous works on self-training
leverage fine-tuned conversational models to automatically generate
pseudo-labeled MR-to-Text pairs for further fine-tuning. However, some
self-augmented data may be noisy or uninformative for the model to learn from.
In this work, we propose a two-phase self-augmentation procedure to generate
high-quality pseudo-labeled MR-to-Text pairs: the first phase selects the most
informative MRs …

arxiv augmentation self-training training