Constructing such grammars is time consuming and error-prone and requires extensive linguistic knowledge and programming proficiency. Recently, with the rise of machine learning and especially deep learning techniques, researchers are starting to bring more data-driven approaches to this field<cite> (Sproat and Jaitly, 2016)</cite> . In this paper, we present our approach to nonstandard text normalization via machine translation techniques, where the source and target are written and spoken form text, respectively.