How Sequence-to-Sequence Models Perceive Language Styles?
Table 2: Performance of different style transfer methods on standard benchmarks.
['Model', 'Yelp Acc.', 'Yelp BLEU', 'Yelp G-Score', 'Yelp Mean', 'Amazon Acc.', 'Amazon BLEU', 'Amazon G-Score', 'Amazon Mean']
Since the trade-off between the transfer ability and content preservation can be controlled, it is hard to select one balanced point to fully characterize the performance of our method. As a complement, we suggest to use Mean as an overall performance measure, which is more stable than G-Score as observed in Fig. As we can see, our method achieved comparable performance with two baselines while averagely outperformed them on Amazon, the benchmark with a larger vocabulary size. For an illustrative comparison, we further provide some generated samples from each method in Appendix A.