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        <title>Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data</title>
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    <name type="personal">
        <namePart type="given">Michael</namePart>
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            <title>Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP</title>
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        <name type="personal">
            <namePart type="given">Reza</namePart>
            <namePart type="family">Haffari</namePart>
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        <name type="personal">
            <namePart type="given">Colin</namePart>
            <namePart type="family">Cherry</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">George</namePart>
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        <name type="personal">
            <namePart type="given">Shahram</namePart>
            <namePart type="family">Khadivi</namePart>
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        <name type="personal">
            <namePart type="given">Bahar</namePart>
            <namePart type="family">Salehi</namePart>
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    <abstract>Manually labeled corpora are expensive to create and often not available for low-resource languages or domains. Automatic labeling approaches are an alternative way to obtain labeled data in a quicker and cheaper way. However, these labels often contain more errors which can deteriorate a classifier’s performance when trained on this data. We propose a noise layer that is added to a neural network architecture. This allows modeling the noise and train on a combination of clean and noisy data. We show that in a low-resource NER task we can improve performance by up to 35% by using additional, noisy data and handling the noise.</abstract>
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    <identifier type="doi">10.18653/v1/W18-3402</identifier>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data
%A Hedderich, Michael A.
%A Klakow, Dietrich
%Y Haffari, Reza
%Y Cherry, Colin
%Y Foster, George
%Y Khadivi, Shahram
%Y Salehi, Bahar
%S Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne
%F hedderich-klakow-2018-training
%X Manually labeled corpora are expensive to create and often not available for low-resource languages or domains. Automatic labeling approaches are an alternative way to obtain labeled data in a quicker and cheaper way. However, these labels often contain more errors which can deteriorate a classifier’s performance when trained on this data. We propose a noise layer that is added to a neural network architecture. This allows modeling the noise and train on a combination of clean and noisy data. We show that in a low-resource NER task we can improve performance by up to 35% by using additional, noisy data and handling the noise.
%R 10.18653/v1/W18-3402
%U https://aclanthology.org/W18-3402/
%U https://doi.org/10.18653/v1/W18-3402
%P 12-18

Markdown (Informal)

[Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data](https://aclanthology.org/W18-3402/) (Hedderich & Klakow, ACL 2018)

• Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data (Hedderich & Klakow, ACL 2018)

ACL

• Michael A. Hedderich and Dietrich Klakow. 2018. Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data. In Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP, pages 12–18, Melbourne. Association for Computational Linguistics.