Language Style Transfer from Sentences with Arbitrary Unknown Styles
Table 3: Testing accuracies on Yelp with Neu+Neg source data.
['#Neural samples', 'STB (with Cyc)', 'Ours']
Neu+Neg as Source Data: The dataset Pos+Neg is not so challenging because we can use a pre-trained discriminator similar to Ds in our model, to remove those samples classified as positive with high probabilities, so that only sentences with a less positive sentiment remain in the source domain. Thus, we test on our second dataset Neu+Neg. In this setting, in case that some positive sentences exist in those neutral reviews, when STB is trained, we use the same pre-trained discriminator in our model to filter out samples classified as positive with probabilities larger than 0.9. In comparison, our model uses all data, since it naturally allows for those data with styles similar to the target style.