Shattering Language Barriers: Singlish to English Translation with Transformer Neural Network

Abstract

This paper presents an innovative sequence-to-sequence machine translation system that leverages the state-of-the-art Transformer neural network architecture to translate sentences from Singlish to English. Notably, this marks the first Singlish-to-English machine translation system developed utilizing deep neural networks. The user-input sentence undergoes a systematic transformation, encompassing vectorization, positional embedding, and translation through the self-attention mechanisms, an innovation introduced by Google in 2017. Unlike dominant sequence transduction models reliant on intricate traditional recurrent or convolutional neural networks featuring encoders and decoders, the proposed model adopts the Transformer architecture, which relies exclusively on attention mechanisms. This innovative approach eschews the need for traditional recurrent and convolutional layers, offering enhanced translation quality, improved parallelization, and significantly reduced training time.  The primary objective of this translator is to facilitate seamless translation, bridging the linguistic gap for both local and international users, thus dismantling language barriers. Impressively, the system demonstrates the capability to translate sentences containing over 20 tokens in less than one seconds. This achievement was made possible through the use of a minimal set of language rules and vocabulary for both the source and target languages.