CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation
Table 7: The performance comparison on EN.
['Method', 'SeqGAN', 'RankGAN', 'LeakGAN', 'RelGAN', 'CatGAN']
The real data experiments are conducted on the EN dataset. After the preprocessing, EN contains 5,255 unique words with the maximum sentence length 51. Under the premise of achieving better BLEU scores, our model improves NLLgen and NLLdiv by 0.04 and 0.021 than the best performance of the baselines, which shows CatGAN can maintain the higher sample quality and diversity simultaneously.