Language Style Transfer by Tianxiao Shen
This thesis studies style transfer on the basis of non parallel text. This is an instance of a broad family of problems including machine translation, decipherment, and attribute modification. The key challenge is to separate the content from style in an unsupervised manner. We assume a shared latent content distribution across different text corpora, and propose a method that leverages refined alignment of latent representations to perform style transfer. The transferred sentences from one style should match example sentences from the other style as a population. To demonstrate the flexibility of the proposed model, we test it on three tasks: sentiment modification, decipherment of word substitution ciphers, and word order recovery. In both automatic and human evaluation our method achieves strong performance. Thesis Supervisor: Regina Barzilay Title: Professor of Electrical Engineering and Computer Science