Neural table-to-text generation approaches are data-hungry, limiting their
adaptation for low-resource real-world applications. Previous works mostly
resort to Pre-trained Language Models (PLMs) to generate fluent summaries of a
table. However, they often contain hallucinated contents due to the
uncontrolled nature of PLMs. Moreover, the topological differences between
tables and sequences are rarely studied. Last but not least, fine-tuning on
PLMs with a handful of instances may lead to over-fitting and catastrophic
forgetting. To alleviate these problems, we propose a prompt-based approach,
Prefix-Controlled Generator (i.e., PCG), for few-shot table-to-text generation.
We prepend a task-specific prefix for a PLM to make the table structure better
fit the pre-trained input. In addition, we generate an input-specific prefix to
control the factual contents and word order of the generated text. Both
automatic and human evaluations on different domains (humans, books and songs)
of the Wikibio dataset show substantial improvements over baseline approaches.

本文提出了一种前缀控制生成器 (Prefix-Controlled Generator) 的方法，应用于神经表格文本生成中的少样本学习问题，该方法使用预训练语言模型，并在输入处加入特定前缀以更好地适应表格结构，同时通过输入特定前缀控制生成的文本的内容和单词顺序，基于 Wikibio 数据集的人类、书籍和歌曲等不同领域的自动和人工评估结果均表明，该方法相对于基线方法取得了实质性的改进。