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Thank 'Goodness'! A Way to Measure Style in Student Essays

机译:谢天谢地'!一种测量学生论文的方法

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Essays have two major components for scoring - content and style. In this paper, we describe a property of the essay, called goodness, and use it to predict the score given for the style of student essays. We compare our approach to solve this problem with baseline approaches, such as language modeling and also a state-of-the-art deep learning system, proposed by Taghipour and Ng (2016). We show that, despite being quite intuitive, our approach is very powerful in predicting the style of the essays.
机译:散文有两个主要成分用于得分 - 内容和风格。在本文中,我们描述了文章的财产,称为善良,并使用它来预测对学生散文风格的分数。我们比较我们用基线方法解决这个问题的方法,例如语言建模以及艺术型深学习系统,由Taghipour和NG(2016年)提出。我们表明,尽管存在完全直观,但我们的方法非常强大,以预测散文的风格。

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