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.

为了解决印地语交互下，由于语言间巨大的形态学复杂度而导致的低资源语言之间的翻译问题，本文提出了一种基于常见多语种拉丁编码的方法。使用这种方法，结合 Byte Pair Embedding，可以更好地利用印地语相似性，并通过将不同但相似的语言映射到同一正交 - 语音字符空间来改进翻译质量。在低资源条件下，我们验证了提出的方法，并且在大多数情况下得到了改进，在类似语言对（Gujarati-Hindi，Marathi-Hindi，Nepali-Hindi，Maithili - Hindi，Punjabi-Hindi 和 Urdu-Hindi）中，在一个案例中提高了约 10 BLEU 分数，而在远程和零 - shot 语言对中还提高了约 1 个 BLEU 分数。