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        <title>Neural-based Tamil Grammar Error Detection</title>
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    <name type="personal">
        <namePart type="given">Dineskumar</namePart>
        <namePart type="family">Murugesapillai</namePart>
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    <name type="personal">
        <namePart type="given">Anankan</namePart>
        <namePart type="family">Ravinthirarasa</namePart>
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    <name type="personal">
        <namePart type="given">Gihan</namePart>
        <namePart type="family">Dias</namePart>
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    <name type="personal">
        <namePart type="given">Kengatharaiyer</namePart>
        <namePart type="family">Sarveswaran</namePart>
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            <title>Proceedings of the First Workshop on Parsing and its Applications for Indian Languages</title>
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            <namePart type="given">Kengatharaiyer</namePart>
            <namePart type="family">Sarveswaran</namePart>
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        <name type="personal">
            <namePart type="given">Parameswari</namePart>
            <namePart type="family">Krishnamurthy</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Pruthwik</namePart>
            <namePart type="family">Mishra</namePart>
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            <publisher>NLP Association of India (NLPAI)</publisher>
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    <abstract>This paper describes an ongoing development of a grammar error checker for the Tamil language using a state-of-the-art deep neural-based approach. This proposed checker capture a vital type of grammar error called subject-predicate agreement errors. In this case, we specifically target the agreement error that occurs between nominal subject and verbal predicates. We also created the first-ever grammar error annotated corpus for Tamil. In addition, we experimented with different multi-lingual pre-trained language models to capture syntactic information and found that IndicBERT gives better performance for our tasks. We implemented this grammar checker as a multi-class classification on top of the IndicBERT pre-trained model, which we fine-tuned using our annotated data. This baseline model gives an F1 Score of 73.4. We are now in the process of improving this proposed system with the use of a dependency parser.</abstract>
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        <url>https://aclanthology.org/2021.pail-1.4</url>
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        <date>2021-12</date>
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            <start>27</start>
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%0 Conference Proceedings
%T Neural-based Tamil Grammar Error Detection
%A Murugesapillai, Dineskumar
%A Ravinthirarasa, Anankan
%A Dias, Gihan
%A Sarveswaran, Kengatharaiyer
%Y Sarveswaran, Kengatharaiyer
%Y Krishnamurthy, Parameswari
%Y Mishra, Pruthwik
%S Proceedings of the First Workshop on Parsing and its Applications for Indian Languages
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C NIT Silchar, India
%F murugesapillai-etal-2021-neural-based
%X This paper describes an ongoing development of a grammar error checker for the Tamil language using a state-of-the-art deep neural-based approach. This proposed checker capture a vital type of grammar error called subject-predicate agreement errors. In this case, we specifically target the agreement error that occurs between nominal subject and verbal predicates. We also created the first-ever grammar error annotated corpus for Tamil. In addition, we experimented with different multi-lingual pre-trained language models to capture syntactic information and found that IndicBERT gives better performance for our tasks. We implemented this grammar checker as a multi-class classification on top of the IndicBERT pre-trained model, which we fine-tuned using our annotated data. This baseline model gives an F1 Score of 73.4. We are now in the process of improving this proposed system with the use of a dependency parser.
%U https://aclanthology.org/2021.pail-1.4
%P 27-32

Markdown (Informal)

[Neural-based Tamil Grammar Error Detection](https://aclanthology.org/2021.pail-1.4) (Murugesapillai et al., PAIL 2021)

• Neural-based Tamil Grammar Error Detection (Murugesapillai et al., PAIL 2021)

ACL

• Dineskumar Murugesapillai, Anankan Ravinthirarasa, Gihan Dias, and Kengatharaiyer Sarveswaran. 2021. Neural-based Tamil Grammar Error Detection. In Proceedings of the First Workshop on Parsing and its Applications for Indian Languages, pages 27–32, NIT Silchar, India. NLP Association of India (NLPAI).