Learning to Generate Multiple Style Transfer Outputs for an Input Sentence
Table 7: Human preference comparison with the BTS on one-to-many style transfer results. The numbers are the user preference score of competing methods.
['Method', 'Style', 'Content', 'Fluency', 'Diversity', 'Overall']
We report the human evaluation with comparisons to different variants of the CAE and BTS. Similar to the human study presented in the main paper, we conduct evaluation using Amazon Mechanical Turk. We randomly sampled 200 sentences from Yelp test set for user study. Each comparison is evaluated by at least three experts whose HIT Approval Rate is greater than 90%. We observed previous models achieve higher style scores, but their output sentences are often in a generic format and may not preserve the content with correct grammar. In contrast, our method achieves significantly better performance than the baselines in terms of diversity, fluency, and overall quality.