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        <title>Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER</title>
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        <namePart type="given">Rudra</namePart>
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        <namePart type="family">Kunchukuttan</namePart>
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    <name type="personal">
        <namePart type="given">Pushpak</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages. Typically, the goal is improving the NER performance of one of the languages (the primary language) using the other assisting languages. We show that the divergence in the tag distributions of the common named entities between the primary and assisting languages can reduce the effectiveness of multilingual learning. To alleviate this problem, we propose a metric based on symmetric KL divergence to filter out the highly divergent training instances in the assisting language. We empirically show that our data selection strategy improves NER performance in many languages, including those with very limited training data.</abstract>
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    <identifier type="doi">10.18653/v1/P18-2064</identifier>
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        <url>https://aclanthology.org/P18-2064</url>
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%0 Conference Proceedings
%T Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER
%A Murthy, Rudra
%A Kunchukuttan, Anoop
%A Bhattacharyya, Pushpak
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F murthy-etal-2018-judicious
%X Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages. Typically, the goal is improving the NER performance of one of the languages (the primary language) using the other assisting languages. We show that the divergence in the tag distributions of the common named entities between the primary and assisting languages can reduce the effectiveness of multilingual learning. To alleviate this problem, we propose a metric based on symmetric KL divergence to filter out the highly divergent training instances in the assisting language. We empirically show that our data selection strategy improves NER performance in many languages, including those with very limited training data.
%R 10.18653/v1/P18-2064
%U https://aclanthology.org/P18-2064
%U https://doi.org/10.18653/v1/P18-2064
%P 401-406

Markdown (Informal)

[Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER](https://aclanthology.org/P18-2064) (Murthy et al., ACL 2018)

• Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER (Murthy et al., ACL 2018)

ACL

• Rudra Murthy, Anoop Kunchukuttan, and Pushpak Bhattacharyya. 2018. Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 401–406, Melbourne, Australia. Association for Computational Linguistics.