  A large number of significant assets are available online in English, which
is frequently translated into native languages to ease the information sharing
among local people who are not much familiar with English. However, manual
translation is a very tedious, costly, and time-taking process. To this end,
machine translation is an effective approach to convert text to a different
language without any human involvement. Neural machine translation (NMT) is one
of the most proficient translation techniques amongst all existing machine
translation systems. In this paper, we have applied NMT on two of the most
morphological rich Indian languages, i.e. English-Tamil and English-Malayalam.
We proposed a novel NMT model using Multihead self-attention along with
pre-trained Byte-Pair-Encoded (BPE) and MultiBPE embeddings to develop an
efficient translation system that overcomes the OOV (Out Of Vocabulary) problem
for low resourced morphological rich Indian languages which do not have much
translation available online. We also collected corpus from different sources,
addressed the issues with these publicly available data and refined them for
further uses. We used the BLEU score for evaluating our system performance.
Experimental results and survey confirmed that our proposed translator (24.34
and 9.78 BLEU score) outperforms Google translator (9.40 and 5.94 BLEU score)
respectively.
