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    <titleInfo>
        <title>Error syntax aware augmentation of feedback comment generation dataset</title>
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    <name type="personal">
        <namePart type="given">Nikolay</namePart>
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        <namePart type="given">Maria</namePart>
        <namePart type="family">Lysyuk</namePart>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
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    <name type="personal">
        <namePart type="given">Lilya</namePart>
        <namePart type="family">Kazakova</namePart>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
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            <title>Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges</title>
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            <namePart type="given">Simon</namePart>
            <namePart type="family">Mille</namePart>
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    <abstract>This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a span of the error, a system generates an explanatory note that helps the writer (language learner) to improve their writing skills. Our solution is based on fine-tuning the T5 model on the initial dataset augmented according to syntactical dependencies of the words located within indicated error span. The solution of our team ‘nigula’ obtained second place according to manual evaluation by the organizers.</abstract>
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%0 Conference Proceedings
%T Error syntax aware augmentation of feedback comment generation dataset
%A Babakov, Nikolay
%A Lysyuk, Maria
%A Shvets, Alexander
%A Kazakova, Lilya
%A Panchenko, Alexander
%Y Mille, Simon
%S Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F babakov-etal-2023-error
%X This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a span of the error, a system generates an explanatory note that helps the writer (language learner) to improve their writing skills. Our solution is based on fine-tuning the T5 model on the initial dataset augmented according to syntactical dependencies of the words located within indicated error span. The solution of our team ‘nigula’ obtained second place according to manual evaluation by the organizers.
%U https://aclanthology.org/2023.inlg-genchal.6
%P 37-44

Markdown (Informal)

[Error syntax aware augmentation of feedback comment generation dataset](https://aclanthology.org/2023.inlg-genchal.6) (Babakov et al., INLG-SIGDIAL 2023)

• Error syntax aware augmentation of feedback comment generation dataset (Babakov et al., INLG-SIGDIAL 2023)

ACL

• Nikolay Babakov, Maria Lysyuk, Alexander Shvets, Lilya Kazakova, and Alexander Panchenko. 2023. Error syntax aware augmentation of feedback comment generation dataset. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges, pages 37–44, Prague, Czechia. Association for Computational Linguistics.