A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
Table 2: Human evaluation results on two datasets. We show human ratings for and target style accuracy (Sty), content preservation (Con), fluency (Flu) on a 1 to 5 Likert scale. We also calculate the average ratings (Avg) and success rate (Suc) as overall scores.
['[EMPTY]', 'Yelp Sty', 'Yelp Con', 'Yelp Flu', 'Yelp  [BOLD] Avg', 'Yelp  [BOLD] Suc', 'Gyafc Sty', 'Gyafc Con', 'Gyafc Flu', 'Gyafc  [BOLD] Avg', 'Gyafc  [BOLD] Suc']
We find that our model achieves the best average score (Avg). Moreover, our system can generate more than 10% successfully (Suc) transferred instances, averaged on two datasets. And all systems show better results on Yelp than Gyafc, revealing that text formality is more challenging than sentiment transfer.