<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="shivnikar-etal-2020-character">
    <titleInfo>
        <title>A character representation enhanced on-device Intent Classification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sudeep</namePart>
        <namePart type="given">Deepak</namePart>
        <namePart type="family">Shivnikar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Himanshu</namePart>
        <namePart type="family">Arora</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Harichandana</namePart>
        <namePart type="family">B S S</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop on Joint NLP Modelling for Conversational AI @ ICON 2020</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Praveen</namePart>
            <namePart type="given">Kumar</namePart>
            <namePart type="given">G</namePart>
            <namePart type="family">S</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Siddhartha</namePart>
            <namePart type="family">Mukherjee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ranjan</namePart>
            <namePart type="family">Samal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>NLP Association of India (NLPAI)</publisher>
            <place>
                <placeTerm type="text">Patna, India</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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 ~5 MB and low inference time of ~2 milliseconds, which proves its efficiency in a resource-constrained environment.</abstract>
    <identifier type="citekey">shivnikar-etal-2020-character</identifier>
    <location>
        <url>https://aclanthology.org/2020.icon-workshop.6</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>40</start>
            <end>46</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A character representation enhanced on-device Intent Classification
%A Shivnikar, Sudeep Deepak
%A Arora, Himanshu
%A B S S, Harichandana
%Y S, Praveen Kumar G.
%Y Mukherjee, Siddhartha
%Y Samal, Ranjan
%S Proceedings of the Workshop on Joint NLP Modelling for Conversational AI @ ICON 2020
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Patna, India
%F shivnikar-etal-2020-character
%X 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.
%U https://aclanthology.org/2020.icon-workshop.6
%P 40-46

Markdown (Informal)

[A character representation enhanced on-device Intent Classification](https://aclanthology.org/2020.icon-workshop.6) (Shivnikar et al., ICON 2020)

• A character representation enhanced on-device Intent Classification (Shivnikar et al., ICON 2020)

ACL

• Sudeep Deepak Shivnikar, Himanshu Arora, and Harichandana B S S. 2020. A character representation enhanced on-device Intent Classification. In Proceedings of the Workshop on Joint NLP Modelling for Conversational AI @ ICON 2020, pages 40–46, Patna, India. NLP Association of India (NLPAI).