Social media contains valuable information about any individual, political entity, or brand since users posted impromptu and honest opinions which benefit a better data-driven. Social media text is unstructured and contains some issues such as slang, dialect, and short form. Most work addresses the text issue using an approach called text normalization. Text normalization is a preprocessing step to transform noisy words into their standard form and improve the performance of NLP applications, such as Sentiment Analysis, Part-of-speech Tagging, and Name Entity Recognition. However, less work utilizes Neural Machine Translation (NMT) approach to address text problems, specifically for short form and slang. In this work, three different NMT architectures are used to address the text problems. We conducted our experiments by training three different models under the same synthetic dataset. We prepared the dataset by collecting a local Malay news site which contains 46k of sentences and 800k words. The sentences are further processed using rule-based to create a parallel text. We then split the parallel text into training, validation, and test sets. The best model that utilize Transformer architecture achieved 99% accuracy on the validation set which provides an advantage in taking the sentence context during the text normalization process.