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    <titleInfo>
        <title>Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding</title>
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    <name type="personal">
        <namePart type="given">Jihwan</namePart>
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        <namePart type="given">Ruhi</namePart>
        <namePart type="family">Sarikaya</namePart>
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    <name type="personal">
        <namePart type="given">Young-Bum</namePart>
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        <dateIssued>2019-06</dateIssued>
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            <title>Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)</title>
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            <namePart type="given">Anastassia</namePart>
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        <name type="personal">
            <namePart type="given">Michelle</namePart>
            <namePart type="family">Morales</namePart>
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        <name type="personal">
            <namePart type="given">Rohit</namePart>
            <namePart type="family">Kumar</namePart>
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    <abstract>In this paper, we introduce an approach for leveraging available data across multiple locales sharing the same language to 1) improve domain classification model accuracy in Spoken Language Understanding and user experience even if new locales do not have sufficient data and 2) reduce the cost of scaling the domain classifier to a large number of locales. We propose a locale-agnostic universal domain classification model based on selective multi-task learning that learns a joint representation of an utterance over locales with different sets of domains and allows locales to share knowledge selectively depending on the domains. The experimental results demonstrate the effectiveness of our approach on domain classification task in the scenario of multiple locales with imbalanced data and disparate domain sets. The proposed approach outperforms other baselines models especially when classifying locale-specific domains and also low-resourced domains.</abstract>
    <identifier type="citekey">lee-etal-2019-locale</identifier>
    <identifier type="doi">10.18653/v1/N19-2002</identifier>
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        <date>2019-06</date>
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%0 Conference Proceedings
%T Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding
%A Lee, Jihwan
%A Sarikaya, Ruhi
%A Kim, Young-Bum
%Y Loukina, Anastassia
%Y Morales, Michelle
%Y Kumar, Rohit
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F lee-etal-2019-locale
%X In this paper, we introduce an approach for leveraging available data across multiple locales sharing the same language to 1) improve domain classification model accuracy in Spoken Language Understanding and user experience even if new locales do not have sufficient data and 2) reduce the cost of scaling the domain classifier to a large number of locales. We propose a locale-agnostic universal domain classification model based on selective multi-task learning that learns a joint representation of an utterance over locales with different sets of domains and allows locales to share knowledge selectively depending on the domains. The experimental results demonstrate the effectiveness of our approach on domain classification task in the scenario of multiple locales with imbalanced data and disparate domain sets. The proposed approach outperforms other baselines models especially when classifying locale-specific domains and also low-resourced domains.
%R 10.18653/v1/N19-2002
%U https://aclanthology.org/N19-2002
%U https://doi.org/10.18653/v1/N19-2002
%P 9-15

Markdown (Informal)

[Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding](https://aclanthology.org/N19-2002) (Lee et al., NAACL 2019)

• Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding (Lee et al., NAACL 2019)

ACL

• Jihwan Lee, Ruhi Sarikaya, and Young-Bum Kim. 2019. Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pages 9–15, Minneapolis, Minnesota. Association for Computational Linguistics.