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    <titleInfo>
        <title>Feedback comment generation using predicted grammatical terms</title>
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        <namePart type="given">Kunitaka</namePart>
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    <name type="personal">
        <namePart type="given">Manabu</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>
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    <abstract>The purpose of feedback comment generation is to provide useful feedback comments for a wide range of errors in learners’ essays from a language learning perspective. Since it is difficult to obtain appropriate comments at a practical level with rule-based or retrieval- based methods, we explore neural-based gen- erative methods with pre-trained models. We further assume the effectiveness of consider- ing grammatical terms in generating feedback comments. Specifically, this paper proposes T5-based models using predicted grammati- cal terms, submitted to FCG GenChal, and presents their results. By using correct gram- matical terms, our model could improve the BLEU score by 19.0 points, compared with the baseline T5 without grammatical terms on the development dataset. Furthermore, by using predicted grammatical terms, our model could improve the manual evaluation score by 2.33 points, compared with the baseline T5 without grammatical terms on the test dataset.</abstract>
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        <url>https://aclanthology.org/2023.inlg-genchal.12</url>
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        <date>2023-09</date>
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            <start>79</start>
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%0 Conference Proceedings
%T Feedback comment generation using predicted grammatical terms
%A Jimichi, Kunitaka
%A Funakoshi, Kotaro
%A Okumura, Manabu
%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 jimichi-etal-2023-feedback
%X The purpose of feedback comment generation is to provide useful feedback comments for a wide range of errors in learners’ essays from a language learning perspective. Since it is difficult to obtain appropriate comments at a practical level with rule-based or retrieval- based methods, we explore neural-based gen- erative methods with pre-trained models. We further assume the effectiveness of consider- ing grammatical terms in generating feedback comments. Specifically, this paper proposes T5-based models using predicted grammati- cal terms, submitted to FCG GenChal, and presents their results. By using correct gram- matical terms, our model could improve the BLEU score by 19.0 points, compared with the baseline T5 without grammatical terms on the development dataset. Furthermore, by using predicted grammatical terms, our model could improve the manual evaluation score by 2.33 points, compared with the baseline T5 without grammatical terms on the test dataset.
%U https://aclanthology.org/2023.inlg-genchal.12
%P 79-83

Markdown (Informal)

[Feedback comment generation using predicted grammatical terms](https://aclanthology.org/2023.inlg-genchal.12) (Jimichi et al., INLG-SIGDIAL 2023)

• Feedback comment generation using predicted grammatical terms (Jimichi et al., INLG-SIGDIAL 2023)

ACL

• Kunitaka Jimichi, Kotaro Funakoshi, and Manabu Okumura. 2023. Feedback comment generation using predicted grammatical terms. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges, pages 79–83, Prague, Czechia. Association for Computational Linguistics.