Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization

• URL: http://arxiv.org/abs/2302.04415v3
• Date: Thu, 17 Aug 2023 01:51:48 GMT
• Title: Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization
• Authors: Zhixin Guo, Minyxuan Yan, Jiexing Qi, Jianping Zhou, Ziwei He, Zhouhan Lin, Guanjie Zheng and Xinbing Wang
• Abstract summary: We suggest a new framework: PromptMize, which targets table-to-text generation under few-shot settings. The design of our framework consists of two aspects: a prompt planner and a knowledge adapter. Our model achieves remarkable performance in generating quality as judged by human and automatic evaluations.
• Score: 41.20314472839442
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pre-trained language models (PLM) have achieved remarkable advancement in table-to-text generation tasks. However, the lack of labeled domain-specific knowledge and the topology gap between tabular data and text make it difficult for PLMs to yield faithful text. Low-resource generation likewise faces unique challenges in this domain. Inspired by how humans descript tabular data with prior knowledge, we suggest a new framework: PromptMize, which targets table-to-text generation under few-shot settings. The design of our framework consists of two aspects: a prompt planner and a knowledge adapter. The prompt planner aims to generate a prompt signal that provides instance guidance for PLMs to bridge the topology gap between tabular data and text. Moreover, the knowledge adapter memorizes domain-specific knowledge from the unlabelled corpus to supply essential information during generation. Extensive experiments and analyses are investigated on three open domain few-shot NLG datasets: human, song, and book. Compared with previous state-of-the-art approaches, our model achieves remarkable performance in generating quality as judged by human and automatic evaluations.

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