  The use of subword embedding has proved to be a major innovation in Neural
Machine Translation (NMT). It helps NMT to learn better context vectors for Low
Resource Languages (LRLs) so as to predict the target words by better modelling
the morphologies of the two languages and also the morphosyntax transfer. Even
so, their performance for translation in Indian language to Indian language
scenario is still not as good as for resource-rich languages. One reason for
this is the relative morphological richness of Indian languages, while another
is that most of them fall into the extremely low resource or zero-shot
categories. Since most major Indian languages use Indic or Brahmi origin
scripts, the text written in them is highly phonetic in nature and phonetically
similar in terms of abstract letters and their arrangements. We use these
characteristics of Indian languages and their scripts to propose an approach
based on common multilingual Latin-based encodings (WX notation) that take
advantage of language similarity while addressing the morphological complexity
issue in NMT. These multilingual Latin-based encodings in NMT, together with
Byte Pair Embedding (BPE) allow us to better exploit their phonetic and
orthographic as well as lexical similarities to improve the translation quality
by projecting different but similar languages on the same orthographic-phonetic
character space. We verify the proposed approach by demonstrating experiments
on similar language pairs (Gujarati-Hindi, Marathi-Hindi, Nepali-Hindi,
Maithili-Hindi, Punjabi-Hindi, and Urdu-Hindi) under low resource conditions.
The proposed approach shows an improvement in a majority of cases, in one case
as much as ~10 BLEU points compared to baseline techniques for similar language
pairs. We also get up to ~1 BLEU points improvement on distant and zero-shot
language pairs.
