English to Arabic Braille Neural Machine Translation Through Corpus Augmentation

Abstract

In this paper we have shown the development of English to Arabic Braille Neural Machine Translation (NMT) System. For our experiments we have developed two NMT systems. The first was the baseline NMT system which was trained only on the English-Arabic parallel corpus whereas the second NMT system has some sub modules for handling syntax transfer of English to Arabic and translation/transliteration of English Named Entities into Arabic equivalents. Then the system used this code-mixed text to train the NMT model. The English-Arabic parallel corpus was taken from Opus parallel corpus repository. For preprocessing of the English text Stanford's Stanza NLP Library was used. The trained NMT models were evaluated using standard automatic MT evaluation metrics and their results were also corelated with human judgements. It was found that the corpus augmented NMT system performed better than the baseline NMT system.