Few-Shot Table-to-Text Generation with Prototype Memory
Yixuan Su, Zaiqiao Meng, Simon Baker, Nigel Collier · 2021
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.