A Systematic Study of Hindi–Dogri Text Translation Using Neural Machine Translation
Keywords:
Neural Machine Translation, Low-Resource Languages, Pivot-Based Translation, chrF Score, SacreBLEU, BLEU Score, Transfer Learning, Hindi Pivot, Dogri-to-English TranslationAbstract
While there have been notable steps taken towards Neural Machine Translation (NMT) for Indic Languages, the gap in precise translation output is still considerable. Unlike any other language, the primary reason for this gap is the lack of in-depth parallel corpora which causes refrains in its development. As a outcome, most Indian languages are still categorized as recourse depleted or untouched languages. To alleviate these barriers, there are multiple state of the art NMT frameworks that have been designed, with the pivot-based approach emerging as the most popular one. This approach, which is based on redistribution strategy, uses a mid-sentence language to ease the burden of translation. For the problem of translating between Dogri and English, a pivot based NMT framework is proposed which uses Hindi as a primary language. The pivot strategy is especially useful because Dogri is a low-resource language and has significant linguistic connections with Hindi. Furthermore, availability of systems that perform accurately during Hindi to English transitions solves the issue efficiently. The model provides a BLEU score of 20.97, a Sacre BLEU score of 21.54 and a chrF score of 50.01, proving that it is efficient for low resourced language translation.
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