Neural Machine Translation (NMT) is a new technique for machine translation
that has led to remarkable improvements compared to rule-based and statistical
machine translation (SMT) techniques, by overcoming many of the weaknesses in
the conventional techniques. We study and apply NMT techniques to create a
system with multiple models which we then apply for six Indian language pairs.
We compare the performances of our NMT models with our system using automatic
evaluation metrics such as UNK Count, METEOR, F-Measure, and BLEU. We find
that NMT techniques are very effective for machine translations of Indian
language pairs. We then demonstrate that we can achieve good accuracy even
using a shallow network; on comparing the performance of Google Translate on
our test dataset, our best model outperformed Google Translate by a margin of
17 BLEU points on Urdu-Hindi, 29 BLEU points on Punjabi-Hindi, and 30 BLEU
points on Gujarati-Hindi translations.