Neural Machine Translation model for University Email Application

TL;DR

This paper presents a regional vocabulary-based Neural Machine Translation model tailored for university email communication, outperforming Google Translate in BLEU scores, especially for Malay-English translation.

Contribution

It introduces a specialized NMT model for university emails that effectively captures regional vocabulary, demonstrating improved translation quality over Google Translate.

Findings

Our model achieves higher BLEU scores than Google Translate.

Regional vocabulary enhances translation accuracy for university emails.

Malay-English translation remains challenging due to language complexity.

Abstract

Machine translation has many applications such as news translation, email translation, official letter translation etc. Commercial translators, e.g. Google Translation lags in regional vocabulary and are unable to learn the bilingual text in the source and target languages within the input. In this paper, a regional vocabulary-based application-oriented Neural Machine Translation (NMT) model is proposed over the data set of emails used at the University for communication over a period of three years. A state-of-the-art Sequence-to-Sequence Neural Network for ML -> EN and EN -> ML translations is compared with Google Translate using Gated Recurrent Unit Recurrent Neural Network machine translation model with attention decoder. The low BLEU score of Google Translation in comparison to our model indicates that the application based regional models are better. The low BLEU score of EN -> ML of our model and Google Translation indicates that the Malay Language has complex language features corresponding to English.