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    <titleInfo>
        <title>Robust to Noise Models in Natural Language Processing Tasks</title>
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    <name type="personal">
        <namePart type="given">Valentin</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Khashabi</namePart>
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    <abstract>There are a lot of noise texts surrounding a person in modern life. The traditional approach is to use spelling correction, yet the existing solutions are far from perfect. We propose robust to noise word embeddings model, which outperforms existing commonly used models, like fasttext and word2vec in different tasks. In addition, we investigate the noise robustness of current models in different natural language processing tasks. We propose extensions for modern models in three downstream tasks, i.e. text classification, named entity recognition and aspect extraction, which shows improvement in noise robustness over existing solutions.</abstract>
    <identifier type="citekey">malykh-2019-robust</identifier>
    <identifier type="doi">10.18653/v1/P19-2002</identifier>
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        <url>https://aclanthology.org/P19-2002</url>
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%0 Conference Proceedings
%T Robust to Noise Models in Natural Language Processing Tasks
%A Malykh, Valentin
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F malykh-2019-robust
%X There are a lot of noise texts surrounding a person in modern life. The traditional approach is to use spelling correction, yet the existing solutions are far from perfect. We propose robust to noise word embeddings model, which outperforms existing commonly used models, like fasttext and word2vec in different tasks. In addition, we investigate the noise robustness of current models in different natural language processing tasks. We propose extensions for modern models in three downstream tasks, i.e. text classification, named entity recognition and aspect extraction, which shows improvement in noise robustness over existing solutions.
%R 10.18653/v1/P19-2002
%U https://aclanthology.org/P19-2002
%U https://doi.org/10.18653/v1/P19-2002
%P 10-16

Markdown (Informal)

[Robust to Noise Models in Natural Language Processing Tasks](https://aclanthology.org/P19-2002) (Malykh, ACL 2019)

• Robust to Noise Models in Natural Language Processing Tasks (Malykh, ACL 2019)

ACL

• Valentin Malykh. 2019. Robust to Noise Models in Natural Language Processing Tasks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 10–16, Florence, Italy. Association for Computational Linguistics.