Neural Machine Translation for Low resource English Bangla
Neural machine translation has recently been able to gain state of the art translation quality for many language pairs. However, neural machine translation has been less tested for English Bangla language pair, two linguistically distant and widely spoken languages. In this paper, we apply neural machine translation to the task of English Bangla translation in both directions and compare it against a standard phrase based statistical machine translation system. We obtain up to +0.30 and +4.95 BLEU improvement over phrase based statistical machine translation for English to Bangla and Bangla to English respectively. Due to low resource and morphological richness of Bangla, English Bangla translation task produces a large number of rare words. We apply subword segmentation with byte pair encoding to handle this rare words issue. We obtain up to +0.69 and +0.30 BLEU improvement over baseline neural machine translation for English to Bangla and Bangla to English respectively. We further investigate our system output for several challenging linguistic properties like subject verb agreement, noun inflection, long distance reordering and rare words translation. We observe that neural machine translation with and without subword segmentation significantly outperform the phrase based statistical machine translation system, thus establishing itself as the state of the art technology for low resource English Bangla machine translation.
Journal of Computer Science