Unsupervised Text Style Transfer using Language Models as Discriminators
Table 2: Results for sentiment modification. X=negative,Y=positive. PPLx denotes the perplexity of sentences transferred from positive sentences evaluated by a language model trained with negative sentences and vice versa.
['Model', 'Accu', 'BLEU', 'PPL [BOLD] X', 'PPL [BOLD] Y']
Results: We report the results in Table. As a baseline, the original corpus has perplexity of 35.8 and 38.8 for the negative and positive sentences respectively. This demonstrates the effectiveness of using LM as the discriminator. has the highest accuracy and BLEU score among the three models while the perplexity is very high. It is not surprising that the classifier will only modify the features of the sentences that are related to the sentiment and there is no mechanism to ensure that the modified sentence being fluent. Hence the corresponding perplexity is very high.