Adapting Knowledge for Few-shot Table-to-Text Generation

• URL: http://arxiv.org/abs/2302.12468v3
• Date: Wed, 27 Mar 2024 06:46:56 GMT
• Title: Adapting Knowledge for Few-shot Table-to-Text Generation
• Authors: Zhixin Guo, Minyxuan Yan, Jiexing Qi, Jianping Zhou, Ziwei He, Guanjie Zheng, Xinbing Wang,
• Abstract summary: We propose a novel framework: Adapt-Knowledge-to-Generate (AKG) AKG adapts unlabeled domain-specific knowledge into the model, which brings at least three benefits. Our model achieves superior performance in terms of both fluency and accuracy as judged by human and automatic evaluations.
• Score: 35.59842534346997
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pretrained language models (PLMs) have made remarkable progress in table-to-text generation tasks. However, the lack of domain-specific knowledge makes it challenging to bridge the topological gap between tabular data and text, especially in real-world applications with limited resources. To mitigate the limitation of insufficient labeled data, we propose a novel framework: Adapt-Knowledge-to-Generate (AKG). The core insight of AKG is to adapt unlabeled domain-specific knowledge into the model, which brings at least three benefits: (1) it injects representation of normal table-related descriptions to bridge the topological gap between tabular data and texts; (2) it enables us to use large amounts of unlabeled domain-specific knowledge fully, which can alleviate the PLMs' inherent shortcomings of lacking domain knowledge; (3) it allows us to design various tasks to employ the domain-specific knowledge. Extensive experiments and analyses are conducted on three open-domain, few-shot natural language generation (NLG) data sets: Humans, Songs, and Books. Compared to previous state-of-the-art approaches, our model achieves superior performance in terms of both fluency and accuracy as judged by human and automatic evaluations.

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