Unpaired Sentiment-to-Sentiment Translation: A CycledReinforcement Learning Approach
Table 1: Automatic evaluations of the proposed method and baselines. ACC evaluates sentiment transformation. BLEU evaluates content preservation. G-score is the geometric mean of ACC and BLEU.
['Yelp', 'ACC', 'BLEU', 'G-score']
ACC evaluates sentiment transformation. BLEU evaluates semantic content preservation. G-score represents the geometric mean of ACC and BLEU. CAAE and MDAL achieve much lower BLEU scores, 1.17 and 1.64 on the Yelp dataset, 0.56 and 0.27 on the Amazon dataset. The low BLEU scores indicate the worrying content preservation performance to some extent. Even with the desired sentiment, the irrelevant generated text leads to worse overall performance. In general, these two systems work more like sentiment-aware language models that generate text only based on the target sentiment and neglect the source input. The main reason is that these two systems attempt to separate emotional information from non-emotional content in a hidden layer, where all information is complicatedly mixed together. It is difficult to only modify emotional information without any loss of non-emotional semantic content.