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Studies of writing revisions rarely focus on revision quality. To addressthis issue, we introduce a corpus of between-draft revisions of studentargumentative essays, annotated as to whether each revision improves essayquality. We demonstrate a potential usage of our annotations by developing amachine learning model to predict revision improvement. With the goal ofexpanding training data, we also extract revisions from a dataset edited byexpert proofreaders. Our results indicate that blending expert and non-expertrevisions increases model performance, with expert data particularly importantfor predicting low-quality revisions.

Annotation and Classification of Sentence-level Revision Improvement