Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization
This addresses the challenge of low-resource table-to-text generation for natural language processing applications, but it is incremental as it builds on existing pre-trained language models.
The paper tackles the problem of generating faithful text from tabular data in few-shot settings by proposing PromptMize, a framework with a prompt planner and knowledge adapter, which achieves remarkable performance on three datasets.
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.