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Going deeper: Automatic short-answer grading by combining student and question models

机译：更深入：结合学生模型和问题模型，自动进行简短答案评分

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摘要

As various educational technologies have rapidly become more powerful and more prevalent, especially from the 2010s onward, the demand of automated grading natural language responses has become a major area of research. In this work, we leverage the classic student and domain/question models that are widely used in the field of intelligent tutoring systems to the task of automatic short-answer grading (ASAG). ASAG is the process of applying natural language processing techniques to assess student-authored short answers, and conventional ASAG systems often mainly focus upon student answers, referred as answer-based. In recent years, various deep learning models have gained great popularity in a wide range of domains. While classic machine learning methods have been widely employed to ASAG, as far as we know, deep learning models have not been applied to it probably because the lexical features from short answers provide limited information. In this work, we explore the effectiveness of a deep learning model, deep belief networks (DBN), to the task of ASAG. Overall, our results on a real-world corpus demonstrate that 1) leveraging student and question models to the conventional answer-based approach can greatly enhance the performance of ASAG, and 2) deep learning models such as DBN can be productively applied to the task of ASAG.
机译：随着各种教育技术迅速变得越来越强大和流行，特别是从2010年代开始，对自然语言反应进行自动评分的需求已成为研究的主要领域。在这项工作中，我们利用在智能补习系统领域中广泛使用的经典学生模型和领域/问题模型来完成自动短答案评分（ASAG）的任务。 ASAG是应用自然语言处理技术来评估学生撰写的简短答案的过程，而传统的ASAG系统通常主要关注学生的答案，称为基于答案的答案。近年来，各种深度学习模型在广泛的领域中获得了极大的普及。虽然经典的机器学习方法已广泛应用于ASAG，但据我们所知，深度学习模型尚未应用于它，这可能是因为简短答案的词汇特征提供的信息有限。在这项工作中，我们探索了深度学习模型，深度信念网络（DBN）对ASAG任务的有效性。总体而言，我们在真实语料库上的结果表明：1）利用学生和问题模型采用传统的基于答案的方法可以大大提高ASAG的性能，以及2）可以将DBN等深度学习模型有效地应用于任务ASAG。

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