  Most studies on text classification are focused on the English language.
However, short texts such as SMS are influenced by regional languages. This
makes the automatic text classification task challenging due to the
multilingual, informal, and noisy nature of language in the text. In this work,
we propose a novel multi-cascaded deep learning model called McM for bilingual
SMS classification. McM exploits $n$-gram level information as well as
long-term dependencies of text for learning. Our approach aims to learn a model
without any code-switching indication, lexical normalization, language
translation, or language transliteration. The model relies entirely upon the
text as no external knowledge base is utilized for learning. For this purpose,
a 12 class bilingual text dataset is developed from SMS feedbacks of citizens
on public services containing mixed Roman Urdu and English languages. Our model
achieves high accuracy for classification on this dataset and outperforms the
previous model for multilingual text classification, highlighting language
independence of McM.
