Abstract:
Text generation is a significant task in natural language processing. In order to solve the problem that most of the generated texts lack diversity and the quality of the generated texts is decreased (due to the long length of the generated texts), a transfer vector text generation adversarial network using the Sentences and Words (SW) reward mechanism was born. It is first for generator hierarchical structure design, including pass training module of characteristic vector and generates vector training module. The discriminant model in the actual text vector feature vector is passed to the generator transfer training module to improve the accuracy of long texts generated, generate vector training module receives its generated word sequence. Then, in the training process, Relational Memory Core (RMC) was used to replace the traditional long-short term memory model as the generator, which improved the model's expression ability and captured information. Finally, the SW reward mechanism is adopted to improve the diversity of text generation. The experimental results show that the evaluation indexes of the Generation Adversarial Network Based on Hierarchical Learning with Multi-reward Text(HLMGAN) model are improved compared with the existing models.