Self-augmented Data Selection for Few-shot Dialogue Generation. (arXiv:2205.09661v1 [cs.CL])

The natural language generation (NLG) module in task-oriented dialogue
systems translates structured meaning representations (MRs) into text
responses, which has a great impact on users' experience as the human-machine
interaction interface. However, in practice, developers often only have a few
well-annotated data and confront a high data collection cost to build the NLG
module. In this work, we adopt the self-training framework to deal with the
few-shot MR-to-Text generation problem. We leverage the pre-trained language
model to self-augment many pseudo-labeled …

arxiv augmented data data generation