Annotation and Classification of Sentence-level Revision Improvement
Annotation and Classification of Sentence-level Revision Improvement
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句子级修订改进的标注和分类
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
D. Litman
中科院分区：
文献类型：
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作者：
T. Afrin;D. Litman
对写作修改的研究很少关注修改质量。为了解决这个问题，我们引入了一个语料库的学生议论文的草稿之间的修订，注释是否每个修订提高文章质量。我们通过开发一个机器学习模型来预测修订改进，展示了我们的注释的潜在用途。为了扩展训练数据，我们还从专家校对人员编辑的数据集中提取修订。我们的研究结果表明，混合专家和非专家的修订提高模型的性能，与专家数据预测低质量的修订特别重要。
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.