Neural machine translation approach for Singlish to English translation

Abstract

This dissertation is for a research that aimed at proposing a language model to translate texts written in Singlish to English. Singlish is an alternative writing system for Sinhala language that uses Latin scripts (English Alphabet) instead of using native Sinhala alphabet. This had been a requirement for long period, since many Sri Lankans use this writing method to write product reviews, social media posts and comments etc. This has been tried since couple of years by many research students but the main challenge was to find a proper data set to evaluate deep learning models for this Natural Language Processing (NLP) task. Hence, traditional statistic, rulebased models has been proposed with less data. This research addresses the challenge

of

preparing a data set to evaluate a deep learning approach for this machine translation activity and also to evaluate a seq2seq Neural Machine Translation (NMT) model. The proposed seq2seq model is purely based on the attention mechanism, as it has been used to improve NMT by selectively focusing on parts of the source sentence during translation. The proposed approach can achieve 24.13 BLEU score on Singlish-English by seeing ~0.15 M parallel sentence pairs with ~50 K word vocabulary.