Text Generation Service Model Based on Truth-Guided SeqGAN

The Generative Adversarial Networks (GAN) has been successfully applied to the generation of text content such as poetry and speech, and it is a hot topic in the field of text generation. However, GAN has been facing the problem of training and convergence. For the generation model, this paper redefines on the loss function. The truth-guided method has been added to make the generated text closer to the real data. For the discriminant model, this paper designs a more suitable network structure. The self-attention mechanism has been added to the discrimination network to obtain richer semantic information. Finally, some experiments under different model structures and different parameters indicates the model with truth-guided and self-attention mechanism gets better results.