So Different Yet So Alike! Constrained Unsupervised Text Style Transfer

Anonymous

Abstract: Transferring text from one domain to the other has seen tremendous progress in the recent past. However, these methods do not aim to explicitly maintain constraints such as similar text length, descriptiveness between the source and the translated text. To this end, we introduce two complementary cooperative losses to the generative adversarial network family. Here, both the generator and the critic reduce the contrastive and/or the classification loss aiming to satisfy the constraints. These losses allow lexical, syntactic, and domain-specific consistencies to persist across domains. We demonstrate the effectiveness of our method over multiple benchmark datasets, both with single and multi-attribute transfers. The complimentary cooperative losses also improve text quality across datasets as judged by current, automated generation and human evaluation metrics.