  Neural table-to-text generation models have achieved remarkable progress on
an array of tasks. However, due to the data-hungry nature of neural models,
their performances strongly rely on large-scale training examples, limiting
their applicability in real-world applications. To address this, we propose a
new framework: Prototype-to-Generate (P2G), for table-to-text generation under
the few-shot scenario. The proposed framework utilizes the retrieved
prototypes, which are jointly selected by an IR system and a novel prototype
selector to help the model bridging the structural gap between tables and
texts. Experimental results on three benchmark datasets with three
state-of-the-art models demonstrate that the proposed framework significantly
improves the model performance across various evaluation metrics.
