@inproceedings{shivnikar-etal-2020-character,
    title = "A character representation enhanced on-device Intent Classification",
    author = "Shivnikar, Sudeep Deepak  and
      Arora, Himanshu  and
      B S S, Harichandana",
    editor = "S, Praveen Kumar G  and
      Mukherjee, Siddhartha  and
      Samal, Ranjan",
    booktitle = "Proceedings of the Workshop on Joint NLP Modelling for Conversational AI @ ICON 2020",
    month = dec,
    year = "2020",
    address = "Patna, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.icon-workshop.6/",
    pages = "40--46",
    abstract = "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 {\textasciitilde}5 MB and low inference time of {\textasciitilde}2 milliseconds, which proves its efficiency in a resource-constrained environment."
}