Text Generation from Triple via Generative Adversarial Nets

Text generation plays an influential role in NLP (Natural Language Processing), but this task is still challenging. In this paper, we focus on generating text from a triple (entity, relation, entity), and we propose a new sequence to sequence model via GAN (Generative Adversarial Networks) rather than MLE (Maximum Likelihood Estimate) to avoid exposure bias. In this model, the generator is a Transformer and the discriminator is a Transformer based binary classifier, both of which use encoder-decoder structure. With regard to generator, the input sequence of encoder is a triple, then the decoder generates sentence in sequence. The input of discriminator consists of a triple and its corresponding sentence, and the output denotes the probability of being real sample. In this experiment, we use different metrics including Bleu score, Rouge-L and Perplexity to evaluate similarity, sufficiency and fluency of the text generated by three models on test set. The experimental results prove our model has achieved the best performance.