KETG: A Knowledge Enhanced Text Generation Framework

Yan Cui, Xi Chen, Jiang Qian, Bojin Zhuang, Shaojun Wang, Jing Xiao

Keywords: text generation, knowledge graph

Abstract: Embedding logical knowledge information into text generation is a challenging NLP task. In this paper, we propose a knowledge enhanced text generation (KETG) framework, which incorporates both the knowledge and associated text corpus to address logicality and diversity in text generation. Specifically, we validate our framework on rhetorical text generation from our newly built rhetoric knowledge graph. Experiments show that our framework outperforms baseline models such as Transformer and GPT-2, on rhetorical type control, semantic comprehensibility and diversity.

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