KETG: A Knowledge Enhanced Text Generation Framework
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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Methods
Absolute Position Encodings •
Adam •
Attention Dropout •
BPE •
Cosine Annealing •
Dense Connections •
Discriminative Fine-Tuning •
Dropout •
GELU •
GPT-2 •
Label Smoothing •
Layer Normalization •
Linear Layer •
Linear Warmup With Cosine Annealing •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Transformer •
Weight Decay
