  Deep Learning techniques are powerful in mimicking humans in a particular set
of problems. They have achieved a remarkable performance in complex learning
tasks. Deep learning inspired Neural Machine Translation (NMT) is a proficient
technique that outperforms traditional machine translation. Performing
machine-aided translation on Indic languages has always been a challenging task
considering their rich and diverse grammar. The neural machine translation has
shown quality results compared to the traditional machine translation
approaches. The fully automatic machine translation becomes problematic when it
comes to low-resourced languages, especially with Sanskrit. This paper presents
attention mechanism based neural machine translation by selectively focusing on
a particular part of language sentences during translation. The work shows the
construction of Sanskrit to Hindi bilingual parallel corpus with nearly 10K
samples and having 178,000 tokens. The neural translation model equipped with
an attention mechanism has been trained on Sanskrit to Hindi parallel corpus.
The approach has shown the significance of attention mechanisms to overcome
long-term dependencies, primarily associated with low resources Indic
languages. The paper shows the attention plots on testing data to demonstrate
the alignment between source and translated words. For the evaluation of the
translated sentences, manual score based human evaluation and automatic
evaluation metric based techniques have been adopted. The attention mechanism
based neural translation has achieved 88% accuracy in human evaluation and a
BLEU score of 0.92 on Sanskrit to Hindi translation.
