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.

翻译：预训练语言模型（PLM）在表格到文本生成任务中取得了显著进展。然而，标记领域知识的匮乏以及表格数据与文本之间的拓扑结构差异，使得PLM难以生成忠实度高的文本。低资源生成在此领域同样面临独特挑战。受人类利用先验知识描述表格数据的启发，我们提出了一种新框架：PromptMize，其针对小样本场景下的表格到文本生成任务。该框架的设计包含两个方面：提示规划器与知识适配器。提示规划器旨在生成提示信号，为PLM提供实例引导以弥合表格数据与文本之间的拓扑结构差异。此外，知识适配器从无标注语料库中记忆领域特定知识，在生成过程中提供必要信息。我们在三个开放领域的小样本自然语言生成数据集（人类、歌曲和书籍）上开展了大量实验与分析。与先前最先进方法相比，我们的模型在人工评估与自动评估中均展现出卓越的生成质量性能。