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        <title>NAT: Noise-Aware Training for Robust Neural Sequence Labeling</title>
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    <name type="personal">
        <namePart type="given">Marcin</namePart>
        <namePart type="family">Namysl</namePart>
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        <namePart type="given">Sven</namePart>
        <namePart type="family">Behnke</namePart>
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    <name type="personal">
        <namePart type="given">Joachim</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Joyce</namePart>
            <namePart type="family">Chai</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Natalie</namePart>
            <namePart type="family">Schluter</namePart>
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            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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    <abstract>Sequence labeling systems should perform reliably not only under ideal conditions but also with corrupted inputs—as these systems often process user-generated text or follow an error-prone upstream component. To this end, we formulate the noisy sequence labeling problem, where the input may undergo an unknown noising process and propose two Noise-Aware Training (NAT) objectives that improve robustness of sequence labeling performed on perturbed input: Our data augmentation method trains a neural model using a mixture of clean and noisy samples, whereas our stability training algorithm encourages the model to create a noise-invariant latent representation. We employ a vanilla noise model at training time. For evaluation, we use both the original data and its variants perturbed with real OCR errors and misspellings. Extensive experiments on English and German named entity recognition benchmarks confirmed that NAT consistently improved robustness of popular sequence labeling models, preserving accuracy on the original input. We make our code and data publicly available for the research community.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.138</identifier>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T NAT: Noise-Aware Training for Robust Neural Sequence Labeling
%A Namysl, Marcin
%A Behnke, Sven
%A Köhler, Joachim
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F namysl-etal-2020-nat
%X Sequence labeling systems should perform reliably not only under ideal conditions but also with corrupted inputs—as these systems often process user-generated text or follow an error-prone upstream component. To this end, we formulate the noisy sequence labeling problem, where the input may undergo an unknown noising process and propose two Noise-Aware Training (NAT) objectives that improve robustness of sequence labeling performed on perturbed input: Our data augmentation method trains a neural model using a mixture of clean and noisy samples, whereas our stability training algorithm encourages the model to create a noise-invariant latent representation. We employ a vanilla noise model at training time. For evaluation, we use both the original data and its variants perturbed with real OCR errors and misspellings. Extensive experiments on English and German named entity recognition benchmarks confirmed that NAT consistently improved robustness of popular sequence labeling models, preserving accuracy on the original input. We make our code and data publicly available for the research community.
%R 10.18653/v1/2020.acl-main.138
%U https://aclanthology.org/2020.acl-main.138
%U https://doi.org/10.18653/v1/2020.acl-main.138
%P 1501-1517

Markdown (Informal)

[NAT: Noise-Aware Training for Robust Neural Sequence Labeling](https://aclanthology.org/2020.acl-main.138) (Namysl et al., ACL 2020)

• NAT: Noise-Aware Training for Robust Neural Sequence Labeling (Namysl et al., ACL 2020)

ACL

• Marcin Namysl, Sven Behnke, and Joachim Köhler. 2020. NAT: Noise-Aware Training for Robust Neural Sequence Labeling. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1501–1517, Online. Association for Computational Linguistics.