Politeness Transfer: A Tag and Generate Approach
Table 5: Comparison of different tagger variants for Yelp and Captions datasets
['[EMPTY]', '[BOLD] Yelp Acc', '[BOLD] Yelp BL-r', '[BOLD] Captions Acc', '[BOLD] Captions BL-r']
We provide a comparison of the two variants of the tagger, namely the replace-tagger and add-tagger on two datasets. We also train and compare them with a combined variant. We observe that for Captions, where we transfer a factual (neutral) to romantic/humorous sentence, the add-tagger provides the best accuracy with a relatively negligible drop in bleu scores. On the contrary, for Yelp, where both polarities are clearly defined, the replace-tagger gives the best performance. Interestingly, the accuracy of the add-tagger is ≈50% in the case of Yelp, since adding negative words to a positive sentence or vice-versa neutralizes the classifier scores. Thus, we can use the add-tagger variant for transfer from a polarized class to a neutral class as well.