title: KETG: A Knowledge Enhanced Text Generation Framework
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.
authors: Yan Cui Xi Chen Jiang Qian Bojin Zhuang Shaojun Wang Jing Xiao
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