  Data-to-text generation systems aim to generate text descriptions based on
input data (often represented in the tabular form). A typical system uses huge
training samples for learning the correspondence between tables and texts.
However, large training sets are expensive to obtain, limiting the
applicability of these approaches in real-world scenarios. In this work, we
focus on few-shot data-to-text generation. We observe that, while fine-tuned
pretrained language models may generate plausible sentences, they suffer from
the low semantic coverage problem in the few-shot setting. In other words,
important input slots tend to be missing in the generated text. To this end, we
propose a search-and-learning approach that leverages pretrained language
models but inserts the missing slots to improve the semantic coverage. We
further fine-tune our system based on the search results to smooth out the
search noise, yielding better-quality text and improving inference efficiency
to a large extent. Experiments show that our model achieves high performance on
E2E and WikiBio datasets. Especially, we cover 98.35% of input slots on E2E,
largely alleviating the low coverage problem.
