  Neural Machine Translation models have replaced the conventional phrase based
statistical translation methods since the former takes a generic, scalable,
data-driven approach rather than relying on manual, hand-crafted features. The
neural machine translation system is based on one neural network that is
composed of two parts, one that is responsible for input language sentence and
other part that handles the desired output language sentence. This model based
on encoder-decoder architecture also takes as input the distributed
representations of the source language which enriches the learnt dependencies
and gives a warm start to the network. In this work, we transform Roman-Urdu to
Urdu transliteration into sequence to sequence learning problem. To this end,
we make the following contributions. We create the first ever parallel corpora
of Roman-Urdu to Urdu, create the first ever distributed representation of
Roman-Urdu and present the first neural machine translation model that
transliterates text from Roman-Urdu to Urdu language. Our model has achieved
the state-of-the-art results using BLEU as the evaluation metric. Precisely,
our model is able to correctly predict sentences up to length 10 while
achieving BLEU score of 48.6 on the test set. We are hopeful that our model and
our results shall serve as the baseline for further work in the domain of
neural machine translation for Roman-Urdu to Urdu using distributed
representation.
