Unpaired Sentiment-to-Sentiment Translation: A CycledReinforcement Learning Approach
Table 2: Human evaluations of the proposed method and baselines. Sentiment evaluates sentiment transformation. Semantic evaluates content preservation.
['Yelp', 'Sentiment', 'Semantic', 'G-score']
It can be clearly seen that the proposed method obviously improves semantic preservation. The semantic score is increased from 3.87 to 5.08 on the Yelp dataset, and from 3.22 to 4.67 on the Amazon dataset. In general, our proposed model achieves the best overall performance. Furthermore, it also needs to be noticed that with the large improvement in content preservation, the sentiment accuracy of the proposed method is lower than that of CAAE on the two datasets. It shows that simultaneously promoting sentiment transformation and content preservation remains to be studied further.