Unsupervised Text Style Transfer using Language Models as Discriminators
Table 3: Results for sentiment modification based on the 500 human annotated sentences as ground truth from (Li et al., 2018).
['Model', 'ACCU', 'BLEU', 'PPL [BOLD] X', 'PPL [BOLD] Y']
Their method is feature based and consists of the following steps: (Delete) first, they use the statistics of word frequency to delete the attribute words such as “good, bad” from original sentences, (Retrieve) then they retrieve the most similar sentences from the other corpus based on nearest neighbor search, (Generate) the attribute words from retrieved sentences are combined with the content words of original sentences to generate transferred sentences. The authors provide 500 human annotated sentences as the ground truth of transferred sentences so we measure the BLEU score against those sentences. We can see our model has similar accuracy compared with DeleteAndRetrieve, but has much better BLEU scores and slightly better perplexity.