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    <titleInfo>
        <title>Rules Ruling Neural Networks - Neural vs. Rule-Based Grammar Checking for a Low Resource Language</title>
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    <name type="personal">
        <namePart type="given">Linda</namePart>
        <namePart type="family">Wiechetek</namePart>
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    <name type="personal">
        <namePart type="given">Flammie</namePart>
        <namePart type="family">Pirinen</namePart>
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    <name type="personal">
        <namePart type="given">Mika</namePart>
        <namePart type="family">Hämäläinen</namePart>
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    <name type="personal">
        <namePart type="given">Chiara</namePart>
        <namePart type="family">Argese</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>We investigate both rule-based and machine learning methods for the task of compound error correction and evaluate their efficiency for North Sámi, a low resource language. The lack of error-free data needed for a neural approach is a challenge to the development of these tools, which is not shared by bigger languages. In order to compensate for that, we used a rule-based grammar checker to remove erroneous sentences and insert compound errors by splitting correct compounds. We describe how we set up the error detection rules, and how we train a bi-RNN based neural network. The precision of the rule-based model tested on a corpus with real errors (81.0%) is slightly better than the neural model (79.4%). The rule-based model is also more flexible with regard to fixing specific errors requested by the user community. However, the neural model has a better recall (98%). The results suggest that an approach that combines the advantages of both models would be desirable in the future. Our tools and data sets are open-source and freely available on GitHub and Zenodo.</abstract>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T Rules Ruling Neural Networks - Neural vs. Rule-Based Grammar Checking for a Low Resource Language
%A Wiechetek, Linda
%A Pirinen, Flammie
%A Hämäläinen, Mika
%A Argese, Chiara
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F wiechetek-etal-2021-rules
%X We investigate both rule-based and machine learning methods for the task of compound error correction and evaluate their efficiency for North Sámi, a low resource language. The lack of error-free data needed for a neural approach is a challenge to the development of these tools, which is not shared by bigger languages. In order to compensate for that, we used a rule-based grammar checker to remove erroneous sentences and insert compound errors by splitting correct compounds. We describe how we set up the error detection rules, and how we train a bi-RNN based neural network. The precision of the rule-based model tested on a corpus with real errors (81.0%) is slightly better than the neural model (79.4%). The rule-based model is also more flexible with regard to fixing specific errors requested by the user community. However, the neural model has a better recall (98%). The results suggest that an approach that combines the advantages of both models would be desirable in the future. Our tools and data sets are open-source and freely available on GitHub and Zenodo.
%U https://aclanthology.org/2021.ranlp-1.171
%P 1526-1535

Markdown (Informal)

[Rules Ruling Neural Networks - Neural vs. Rule-Based Grammar Checking for a Low Resource Language](https://aclanthology.org/2021.ranlp-1.171) (Wiechetek et al., RANLP 2021)

• Rules Ruling Neural Networks - Neural vs. Rule-Based Grammar Checking for a Low Resource Language (Wiechetek et al., RANLP 2021)

ACL

• Linda Wiechetek, Flammie Pirinen, Mika Hämäläinen, and Chiara Argese. 2021. Rules Ruling Neural Networks - Neural vs. Rule-Based Grammar Checking for a Low Resource Language. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1526–1535, Held Online. INCOMA Ltd..