  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 based on model's prediction uncertainty; with the selected MRs,
the second phase generates accurate responses by aggregating multiple perturbed
latent representations from each MR. Empirical experiments on two benchmark
datasets, FewShotWOZ and FewShotSGD, show that our method generally outperforms
existing self-training methods on both automatic and human evaluations.
