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        <title>Annotation and Classification of Sentence-level Revision Improvement</title>
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            <title>Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
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        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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            <namePart type="given">Ekaterina</namePart>
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            <namePart type="given">Helen</namePart>
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    <abstract>Studies of writing revisions rarely focus on revision quality. To address this issue, we introduce a corpus of between-draft revisions of student argumentative essays, annotated as to whether each revision improves essay quality. We demonstrate a potential usage of our annotations by developing a machine learning model to predict revision improvement. With the goal of expanding training data, we also extract revisions from a dataset edited by expert proofreaders. Our results indicate that blending expert and non-expert revisions increases model performance, with expert data particularly important for predicting low-quality revisions.</abstract>
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    <identifier type="doi">10.18653/v1/W18-0528</identifier>
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%0 Conference Proceedings
%T Annotation and Classification of Sentence-level Revision Improvement
%A Afrin, Tazin
%A Litman, Diane
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F afrin-litman-2018-annotation
%X Studies of writing revisions rarely focus on revision quality. To address this issue, we introduce a corpus of between-draft revisions of student argumentative essays, annotated as to whether each revision improves essay quality. We demonstrate a potential usage of our annotations by developing a machine learning model to predict revision improvement. With the goal of expanding training data, we also extract revisions from a dataset edited by expert proofreaders. Our results indicate that blending expert and non-expert revisions increases model performance, with expert data particularly important for predicting low-quality revisions.
%R 10.18653/v1/W18-0528
%U https://aclanthology.org/W18-0528
%U https://doi.org/10.18653/v1/W18-0528
%P 240-246

Markdown (Informal)

[Annotation and Classification of Sentence-level Revision Improvement](https://aclanthology.org/W18-0528) (Afrin & Litman, BEA 2018)

• Annotation and Classification of Sentence-level Revision Improvement (Afrin & Litman, BEA 2018)

ACL

• Tazin Afrin and Diane Litman. 2018. Annotation and Classification of Sentence-level Revision Improvement. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 240–246, New Orleans, Louisiana. Association for Computational Linguistics.