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The Effect of Adding Authorship Knowledge in Automated Text Scoring

机译:添加作者知识在自动文本评分中的作用

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Some language exams have multiple writing tasks. When a learner writes multiple texts in a language exam, it is not surprising that the quality of these texts tends to be similar, and the existing automated text scoring (ATS) systems do not explicitly model this similarity. In this paper, we suggest that it could be useful to include the other texts written by this learner in the same exam as extra references in an ATS system. We propose various approaches of fusing information from multiple tasks and pass this authorship knowledge into our ATS model on six different datasets. We show that this can positively affect the model performance in most cases.
机译:有些语言考试有多项写作任务。当学习者在语言考试中写多个文本时,这些文本的质量趋于相似并不奇怪,并且现有的自动文本评分(ATS)系统没有明确为这种相似性建模。在本文中,我们建议将由该学习者撰写的其他教科书包含在同一考试中,作为ATS系统中的其他参考文献可能会很有用。我们提出了多种方法来融合来自多个任务的信息,然后将作者身份知识传递给六个不同数据集的ATS模型。我们表明,在大多数情况下,这可以对模型性能产生积极影响。

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