Our mission is to create value and make a difference in the area of natural language processing.
Language style transfer is a popular task in Natural Language Processing (NLP) which aims to modify the style of a sentence while keeping its content unchanged. Previous work mainly focuses on using adversarial methods, which has struggled to produce high-quality outputs. In this project, we will first evaluate the performance of three most state-of-the-art approaches. We will then explore a new approach, iterative semantic matching, and compare it with previous methods. Our model will be evaluated on three commonly used benchmark data sets: Yelp sentiment data set, formality style data set, and a democratic-versus-republican political slants data set. Our approach can be applied to a wide range of applications, including stylistic dialogue systems, text formality conversion, and partisan news text generation.