Politeness Transfer: A Tag and Generate Approach
Table 2: Results on the Yelp, Amazon and Captions datasets.
['[EMPTY]', '[BOLD] Yelp Acc', '[BOLD] Yelp BL-s', '[BOLD] Yelp BL-r', '[BOLD] Yelp MET', '[BOLD] Yelp ROU', '[BOLD] Amazon Acc', '[BOLD] Amazon BL-s', '[BOLD] Amazon BL-r', '[BOLD] Amazon MET', '[BOLD] Amazon ROU', '[BOLD] Captions Acc', '[BOLD] Captions BL-s', '[BOLD] Captions BL-r', '[BOLD] Captions MET', '[BOLD] Captions ROU']
For each of the datasets our test set comprises 500 samples (with human references) curated by Li et al. We observe an increase in the bleu-reference scores by 5.25, 4.95 and 3.64 on the Yelp, Amazon, and Captions test sets respectively. Additionally, we improve the transfer accuracy for Amazon by 14.2% while achieving accuracies similar to drg on Yelp and Captions. As noted by Li et al. Hence, the performance of their model is worse in this case. Since we don’t make any such assumptions, we perform significantly better on this dataset.