  The recent surge of complex attention-based deep learning architectures has
led to extraordinary results in various downstream NLP tasks in the English
language. However, such research for resource-constrained and morphologically
rich Indian vernacular languages has been relatively limited. This paper
proffers team SPPU\_AKAH's solution for the TechDOfication 2020 subtask-1f:
which focuses on the coarse-grained technical domain identification of short
text documents in Marathi, a Devanagari script-based Indian language. Availing
the large dataset at hand, a hybrid CNN-BiLSTM attention ensemble model is
proposed that competently combines the intermediate sentence representations
generated by the convolutional neural network and the bidirectional long
short-term memory, leading to efficient text classification. Experimental
results show that the proposed model outperforms various baseline machine
learning and deep learning models in the given task, giving the best validation
accuracy of 89.57\% and f1-score of 0.8875. Furthermore, the solution resulted
in the best system submission for this subtask, giving a test accuracy of
64.26\% and f1-score of 0.6157, transcending the performances of other teams as
well as the baseline system given by the organizers of the shared task.
