  Natural Language Generation (NLG) is a key component in a task-oriented
dialogue system, which converts the structured meaning representation (MR) to
the natural language. For large-scale conversational systems, where it is
common to have over hundreds of intents and thousands of slots, neither
template-based approaches nor model-based approaches are scalable. Recently,
neural NLGs started leveraging transfer learning and showed promising results
in few-shot settings. This paper proposes AUGNLG, a novel data augmentation
approach that combines a self-trained neural retrieval model with a few-shot
learned NLU model, to automatically create MR-to-Text data from open-domain
texts. The proposed system mostly outperforms the state-of-the-art methods on
the FewShotWOZ data in both BLEU and Slot Error Rate. We further confirm
improved results on the FewShotSGD data and provide comprehensive analysis
results on key components of our system. Our code and data are available at
https://github.com/XinnuoXu/AugNLG.
