Title: Language Style Transfer

Abstract:  Recent advances in text generation tasks such as machine translation and summarization rely on the use of massive amounts of parallel data, which is costly to collect or nonexistent in many scenarios. In this talk, I will present a novel model to perform style transfer on the basis of non-parallel text. This is an instance of a broad family of problems including machine translation, decipherment, and sentiment modification. I will talk about how we deal with the challenge to disentangle content from style, as well as the techniques we use for adversarial training over discrete samples. I will conclude with the experiments we design which allow qualitative and quantitative evaluation of the effectiveness of our method.