Toward a Task of Feedback Comment Generation for Writing Learning
Toward a Task of Feedback Comment Generation for Writing Learning
复制标题
针对写作学习的反馈评论生成任务
DOI：
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发表时间：
2019
期刊：
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通讯作者：
Ryo Nagata
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文献类型：
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作者：
Ryo Nagata
在本文中，我们介绍了一项称为反馈评论生成的新颖任务——自动生成反馈评论的任务，例如为非英语母语学习者进行写作学习的提示或解释性说明。几乎没有关于此任务的工作，也没有带有反馈评论的语料库。我们迈出了第一步，创建了由大约 1,900 篇文章组成的学习者语料库，其中所有介词错误均通过反馈注释手动注释。我们在数据集上测试了三种基线方法，表明一种简单的基于神经检索的方法设置了 F 度量为 0.34 至 0.41 的基线性能。最后，我们研究了结果，以探讨我们需要进行哪些修改才能获得更好的性能。我们还探讨了这项工作中未解决的问题
In this paper, we introduce a novel task called feedback comment generation — a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning for non-native learners of English. There has been almost no work on this task nor corpus annotated with feedback comments. We have taken the first step by creating learner corpora consisting of approximately 1,900 essays where all preposition errors are manually annotated with feedback comments. We have tested three baseline methods on the dataset, showing that a simple neural retrieval-based method sets a baseline performance with an F-measure of 0.34 to 0.41. Finally, we have looked into the results to explore what modifications we need to make to achieve better performance. We also have explored problems unaddressed in this work