  Code-switching is a common phenomenon among people with diverse lingual
background and is widely used on the internet for communication purposes. In
this paper, we present a Recurrent Neural Network combined with the Attention
Model for Language Identification in Code-Switched Data in English and low
resource Roman Urdu. The attention model enables the architecture to learn the
important features of the languages hence classifying the code switched data.
We demonstrated our approach by comparing the results with state of the art
models i.e. Hidden Markov Models, Conditional Random Field and Bidirectional
LSTM. The models evaluation, using confusion matrix metrics, showed that the
attention mechanism provides improved the precision and accuracy as compared to
the other models.
