CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation
Table 6: The NLLoracle scores on general text generation.
['Length', 'SeqGAN', 'RankGAN', 'LeakGAN', 'RelGAN', 'CatGAN']
The synthetic data experiments run with sequence length 20 and 40, and the ground-truth NLLoracle scores are 5.750 and 4.071, respectively. Compared with all baselines, CatGAN outperforms the best of them by 0.303 and 0.521 on NLLoracle with different sequence length, respectively, which verifies the better sample quality. Specially, with sequence length 40, CatGAN significantly improves the metric by 0.956 and 3.723 over LeakGAN and RankGAN, respectively, and it illustrates that our model is more powerful to catch long-term dependencies. With the help of the hierarchical evolutionary learning algorithm, CatGAN outperforms RelGAN which also employs the temperature and the Gumbel-Softmax relaxation.