CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation
Table 5: The ablation study on AR.
['Method', 'CatGAN w/o H', 'CatGAN w/o T', 'CatGAN w/o O', 'CatGAN']
For illustrating the effectiveness of TMS and OMS, the ablation study is conducted on AR. The whole hierarchical evolutionary learning algorithm is removed as CatGAN w/o H, TMS is removed from CatGAN as CatGAN w/o T, and OMS is replaced with lCatRaGθ to form CatGAN w/o O. Compared with CatGAN, CatGAN w/o T shows the degradation on all BLEU scores and only increases NLLdiv by 0.016, which means the worse quality and the similar diversity, respectively. Although CatGAN w/o O achieves competitive sample diversity over CatGAN, it shows a significant degradation on BLEU, which means OMS can effectively guide our model. CatGAN w/o H gets the worse sample quality than CatGAN w/o O, but it still outperforms SentiGAN. The ablation study illustrates that combining TMS and OMS facilitates generating diversified and high-quality samples on real data.