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.
@article{arxiv.2101.04456,
title = {A character representation enhanced on-device Intent Classification},
author = {Sudeep Deepak Shivnikar and Himanshu Arora and Harichandana B S S},
journal= {arXiv preprint arXiv:2101.04456},
year = {2021}
}
Comments
Accepted for publication in ICON 2020: 17th International Conference on Natural Language Processing