Learning to Generate Multiple Style Transfer Outputs for an Input Sentence
Table 8: Empiricial analysis of the impact of each term in the proposed objective function for the proposed one-to-many style transfer task.
['Recon. Loss', 'Back-Trans. Loss', 'Style Cls. Loss', 'Style Score', 'Content Preservation Score', 'BLEU']
We report the style score and the content preservation score in this experiment. We additionally present the BLEU score Papineni et al. A model with a higher BLEU score means that the model is better in translating reasonable sentences. Training with reconstruction loss works well for content preservation yet it performs less favorably for style transfer. Back-translation loss is able to improve style and content preservation scores since it encourage content and style representations to be disentangle. When training with the style loss, our model improves the style accuracy, yet performs worse on content preservation. Overall, we observe that training with all the objective terms achieves a balanced performance in terms of different evaluation scores. The results show that the reconstruction loss, the back-translation loss, and the style loss are important for style transfer.