CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation
Table 1: The NLLoracle scores on category text generation. For the NLLoracle scores, the lower the better.
['Length', 'SentiGAN', 'CSGAN', 'CatGAN']
The synthetic data experiments are set with sequence length 20 and 40. NLLoracle is used to measure the sample quality, and the ground-truth scores are 5.748 and 4.015 for different sequence length, respectively.