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

Understand

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.