论文标题
通过使用反向翻译论文和调整分数来提高自动论文评分的表现
Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores
论文作者
论文摘要
自动论文评分在评估学生在教育中的语言能力方面起着重要作用。传统方法使用手工制作的功能来得分，并且耗时且复杂。最近，没有任何功能工程，神经网络方法提高了性能。与其他自然语言处理任务不同，只有少数数据集可公开可用于自动化论文评分，并且数据集的大小还不够大。考虑到神经网络的性能与数据集的大小密切相关，因此缺乏数据限制了自动论文评分模型的性能改善。在本文中，我们提出了一种使用反翻译和分数调整来增加论文评分对数的方法，并将其应用于自动化的学生评估奖数据集以进行增强。我们使用先前工作的模型评估了增强数据的有效性。此外，在使用长短期内存的模型中评估了性能，该模型被广泛用于自动化论文评分。通过使用增强数据训练模型来提高模型的性能。
Automated essay scoring plays an important role in judging students' language abilities in education. Traditional approaches use handcrafted features to score and are time-consuming and complicated. Recently, neural network approaches have improved performance without any feature engineering. Unlike other natural language processing tasks, only a small number of datasets are publicly available for automated essay scoring, and the size of the dataset is not sufficiently large. Considering that the performance of a neural network is closely related to the size of the dataset, the lack of data limits the performance improvement of the automated essay scoring model. In this paper, we proposed a method to increase the number of essay-score pairs using back-translation and score adjustment and applied it to the Automated Student Assessment Prize dataset for augmentation. We evaluated the effectiveness of the augmented data using models from prior work. In addition, performance was evaluated in a model using long short-term memory, which is widely used for automated essay scoring. The performance of the models was improved by using augmented data to train the models.
