  Machine translation has gained much attention in recent years. It is a
sub-field of computational linguistic which focus on translating text from one
language to other language. Among different translation techniques, neural
network currently leading the domain with its capabilities of providing a
single large neural network with attention mechanism, sequence-to-sequence and
long-short term modelling. Despite significant progress in domain of machine
translation, translation of out-of-vocabulary words(OOV) which include
technical terms, named-entities, foreign words are still a challenge for
current state-of-art translation systems, and this situation becomes even worse
while translating between low resource languages or languages having different
structures. Due to morphological richness of a language, a word may have
different meninges in different context. In such scenarios, translation of word
is not only enough in order provide the correct/quality translation.
Transliteration is a way to consider the context of word/sentence during
translation. For low resource language like Urdu, it is very difficult to
have/find parallel corpus for transliteration which is large enough to train
the system. In this work, we presented transliteration technique based on
Expectation Maximization (EM) which is un-supervised and language independent.
Systems learns the pattern and out-of-vocabulary (OOV) words from parallel
corpus and there is no need to train it on transliteration corpus explicitly.
This approach is tested on three models of statistical machine translation
(SMT) which include phrasebased, hierarchical phrase-based and factor based
models and two models of neural machine translation which include LSTM and
transformer model.
