CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation
Table 3: The performance comparison on AR.
['Method', 'SentiGAN', 'CSGAN', 'CatGAN']
The real data experiments are conducted on MR and AR. After the same preprocessing, MR consists of 6,216 unique words with the maximum sentence length 15, and AR contains 6,416 unique words with the maximum sentence length 40. On MR, since SentiGAN is designed to generate sentiment text, it shows better results than CSGAN on the BLEU scores. On AR, the sufficient training samples improve the performance of all methods. CSGAN heavily relies on the auxiliary classifier and the RL algorithm, and it shows a significant quality degradation than CatGAN on BLEU while generating long sentences on AR. Compared with the baselines, CatGAN is not limited by the category type of data and obtains the better BLEU scores on both MR and AR, which also shows that CatGAN can catch the dependencies in short and long sentences. Besides, on AR, CatGAN gets 3.104 on NLLgen and 1.539 on NLLdiv, which illustrates that our model can maintain good diversity while significantly improving quality. Actually, optimizing NLLdiv based score function can lead to a better NLLdiv, yet CatGAN still consistently bests NLLdiv and an existing metric, NLLgen.