Style Transfer as Unsupervised Machine Translation
Table 3: Automatic evaluation results on Yelp, Amazon and Captions datasets. “Classifier” shows the accuracy of sentences labeled by the pre-trained style classifier. “BLEU(%)” measures content similarity between the output and the human reference.
['[EMPTY]', 'Yelp Classifier', 'Yelp BLEU', 'Amazon Classifier', 'Amazon BLEU', 'Captions Classifier', 'Captions BLEU']
We can see that CrossAligned obtains high style transfer accuracy but sacrifices content consistency. In addition, MultiDecoder and StyleEmedding can help the preservation of content but reduce the accuracy of style transfer. Compared with previous methods, TemplateBased and Del-Retr-Gen achieve a better balance between the transfer accuracy and the content preservation. Our approach achieves significant improvements over CrossAligned, MultiDecoder, StyleEmedding and Del-Retr-Gen in both transfer accuracy and quality of content preservation. It can be clearly observed that our proposed method achieves the best performance among all systems, with 10%, 8% and 5% point improvements than Del-Retr-Gen on Yelp, Amazon and Captions respectively, which demonstrates the effectiveness of our proposed method. Compared with Del-Retr-Gen, our method is rated higher on all three criteria.