Few-Shot Table-to-Text Generation with Prototype Memory

Yixuan Su; Zaiqiao Meng; Simon Baker; Nigel Collier

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arXiv · 2108.12516

Few-Shot Table-to-Text Generation with Prototype Memory

Yixuan Su·Zaiqiao Meng·Simon Baker·Nigel Collier

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Abstract

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.

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BibTeX RIS

Yixuan Su, Zaiqiao Meng, Simon Baker, Nigel Collier. 2021-08-27. Few-Shot Table-to-Text Generation with Prototype Memory. https://arxiv.org/abs/2108.12516

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