Unsupervised Text Style Transfer using Language Models as Discriminators
Table 1: Decipherment results measured in BLEU. Copy is directly measuring y against x. LM + adv denotes we use negative samples to train the language model.∗We run the code open-sourced by the authors to get the results.
['Model', '20%', '40%', '60%', '80%', '100%']
We can see that using adversarial training sometimes improves the results. However, we found empirically that using negative samples makes the training very unstable and the model diverges easily. This is the main reason why we did not get consistently better results by incorporating adversarial training.