LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training

• URL: http://arxiv.org/abs/2112.01404v3
• Date: Fri, 5 May 2023 03:26:54 GMT
• Title: LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training
• Authors: Shumin Deng, Jiacheng Yang, Hongbin Ye, Chuanqi Tan, Mosha Chen, Songfang Huang, Fei Huang, Huajun Chen, Ningyu Zhang
• Abstract summary: We propose a unified framework for logical knowledge-conditioned text generation in the few-shot setting. Our approach leverages self-training and samples pseudo logical forms based on content and structure consistency.
• Score: 76.90793623822866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural language generation from structured data mainly focuses on surface-level descriptions, suffering from uncontrollable content selection and low fidelity. Previous works leverage logical forms to facilitate logical knowledge-conditioned text generation. Though achieving remarkable progress, they are data-hungry, which makes the adoption for real-world applications challenging with limited data. To this end, this paper proposes a unified framework for logical knowledge-conditioned text generation in the few-shot setting. With only a few seeds logical forms (e.g., 20/100 shot), our approach leverages self-training and samples pseudo logical forms based on content and structure consistency. Experimental results demonstrate that our approach can obtain better few-shot performance than baselines.

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