Neural Machine Translation model for University Email Application
Published in 2020 2nd Symposium on Signal Processing Systems, 2020
Recommended citation: Sandhya Aneja, Siti Nur, Nagender Aneja "Neural Machine Translation model for University Email Application." 2020 2nd Symposium on Signal Processing Systems, 2020. pp. 74--79 doi: 10.1145/3421515.3421522 https://dl.acm.org/doi/10.1145/3421515.3421522
(Conference Publication)
Abstract: Machine translation has many applications such as news translation, email translation, official letter translation, etc. Commercial translators, e.g., Google Translations, lack regional vocabulary and cannot learn the bilingual text in the source and target languages within the input. This paper proposes a regional vocabulary-based application-oriented Neural Machine Translation (NMT) model over the data set of emails used at the University for communication over three years. A state-of-the-art Sequence-to-Sequence Neural Network for ML → EN (Malay to English) and EN → ML (English to Malay) translations are compared with Google Translate using a Gated Recurrent Unit Recurrent Neural Network machine translation model with an attention decoder. The low BLEU score of Google Translation compared to our model indicates that the application-based regional models are better. The low BLEU score of English to Malay of our model and Google Translation indicates that the Malay Language has complex language features corresponding to English.