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Deep Learning techniques are powerful in mimicking humans in a particular setof problems. They have achieved a remarkable performance in complex learningtasks. Deep learning inspired Neural Machine Translation (NMT) is a proficienttechnique that outperforms traditional machine translation. Performingmachine-aided translation on Indic languages has always been a challenging taskconsidering their rich and diverse grammar. The neural machine translation hasshown quality results compared to the traditional machine translationapproaches. The fully automatic machine translation becomes problematic when itcomes to low-resourced languages, especially with Sanskrit. This paper presentsattention mechanism based neural machine translation by selectively focusing ona particular part of language sentences during translation. The work shows theconstruction of Sanskrit to Hindi bilingual parallel corpus with nearly 10Ksamples and having 178,000 tokens. The neural translation model equipped withan attention mechanism has been trained on Sanskrit to Hindi parallel corpus.The approach has shown the significance of attention mechanisms to overcomelong-term dependencies, primarily associated with low resources Indiclanguages. The paper shows the attention plots on testing data to demonstratethe alignment between source and translated words. For the evaluation of thetranslated sentences, manual score based human evaluation and automaticevaluation metric based techniques have been adopted. The attention mechanismbased neural translation has achieved 88% accuracy in human evaluation and aBLEU score of 0.92 on Sanskrit to Hindi translation.

Attention based Sequence to Sequence Learning for Machine Translation of Low Resourced Indic Languages -- A case of Sanskrit to Hindi