  Intent classification is an important task in natural language understanding
systems. Existing approaches have achieved perfect scores on the benchmark
datasets. However they are not suitable for deployment on low-resource devices
like mobiles, tablets, etc. due to their massive model size. Therefore, in this
paper, we present a novel light-weight architecture for intent classification
that can run efficiently on a device. We use character features to enrich the
word representation. Our experiments prove that our proposed model outperforms
existing approaches and achieves state-of-the-art results on benchmark
datasets. We also report that our model has tiny memory footprint of ~5 MB and
low inference time of ~2 milliseconds, which proves its efficiency in a
resource-constrained environment.
