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    <titleInfo>
        <title>Cross-lingual Transfer Learning for Japanese Named Entity Recognition</title>
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    <name type="personal">
        <namePart type="given">Andrew</namePart>
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        <namePart type="given">Penny</namePart>
        <namePart type="family">Karanasou</namePart>
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    <name type="personal">
        <namePart type="given">Judith</namePart>
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    <name type="personal">
        <namePart type="given">Dietrich</namePart>
        <namePart type="family">Klakow</namePart>
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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>
            <namePart type="family">Loukina</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Michelle</namePart>
            <namePart type="family">Morales</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rohit</namePart>
            <namePart type="family">Kumar</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>This work explores cross-lingual transfer learning (TL) for named entity recognition, focusing on bootstrapping Japanese from English. A deep neural network model is adopted and the best combination of weights to transfer is extensively investigated. Moreover, a novel approach is presented that overcomes linguistic differences between this language pair by romanizing a portion of the Japanese input. Experiments are conducted on external datasets, as well as internal large-scale real-world ones. Gains with TL are achieved for all evaluated cases. Finally, the influence on TL of the target dataset size and of the target tagset distribution is further investigated.</abstract>
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    <identifier type="doi">10.18653/v1/N19-2023</identifier>
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%0 Conference Proceedings
%T Cross-lingual Transfer Learning for Japanese Named Entity Recognition
%A Johnson, Andrew
%A Karanasou, Penny
%A Gaspers, Judith
%A Klakow, Dietrich
%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 johnson-etal-2019-cross
%X This work explores cross-lingual transfer learning (TL) for named entity recognition, focusing on bootstrapping Japanese from English. A deep neural network model is adopted and the best combination of weights to transfer is extensively investigated. Moreover, a novel approach is presented that overcomes linguistic differences between this language pair by romanizing a portion of the Japanese input. Experiments are conducted on external datasets, as well as internal large-scale real-world ones. Gains with TL are achieved for all evaluated cases. Finally, the influence on TL of the target dataset size and of the target tagset distribution is further investigated.
%R 10.18653/v1/N19-2023
%U https://aclanthology.org/N19-2023
%U https://doi.org/10.18653/v1/N19-2023
%P 182-189

Markdown (Informal)

[Cross-lingual Transfer Learning for Japanese Named Entity Recognition](https://aclanthology.org/N19-2023) (Johnson et al., NAACL 2019)

• Cross-lingual Transfer Learning for Japanese Named Entity Recognition (Johnson et al., NAACL 2019)

ACL

• Andrew Johnson, Penny Karanasou, Judith Gaspers, and Dietrich Klakow. 2019. Cross-lingual Transfer Learning for Japanese Named Entity Recognition. 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 182–189, Minneapolis, Minnesota. Association for Computational Linguistics.