使用AutoSAS --一个简短答案自动评分系统--进行评分
Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers
摘要：
在MOOC时代，数百万考生参加在线考试，其中简短答案的评分是不可或缺的一部分。由人类评分员对其进行评估变得棘手。因此，应设计和部署一个能够对这些答复进行分级的通用自动化系统。在本文中，我们提出了一种快速，可扩展，准确的方法自动化的简短回答评分（SAS）。我们提出并解释了一个系统的SAS，即AutoSAS的设计和开发。给定一个问题及其评分样本，AutoSAS可以学习成功地对该提示进行评分。本文进一步阐述了词汇多样性，Word 2 Vec，提示和内容重叠等特征，这些特征在构建我们提出的模型中起着关键作用。我们还提出了一种方法，用于指示得分的答案负责的因素。训练模型在广泛使用的公共数据集上进行评估，即自动学生评估奖简短答案评分（ASAP-SAS）。AutoSAS显示出最先进的性能，并在一些问题提示中实现了超过8%的更好结果，如二次加权Kappa（QWK）所测量的，显示出与人类相当的性能。
英文摘要：
In the era of MOOCs, online exams are taken by millions of candidates, where scoring short answers is an integral part. It becomes intractable to evaluate them by human graders. Thus, a generic automated system capable of grading these responses should be designed and deployed. In this paper, we present a fast, scalable, and accurate approach towards automated Short Answer Scoring (SAS). We propose and explain the design and development of a system for SAS, namely AutoSAS. Given a question along with its graded samples, AutoSAS can learn to grade that prompt successfully. This paper further lays down the features such as lexical diversity, Word2Vec, prompt, and content overlap that plays a pivotal role in building our proposed model. We also present a methodology for indicating the factors responsible for scoring an answer. The trained model is evaluated on an extensively used public dataset, namely Automated Student Assessment Prize Short Answer Scoring (ASAP-SAS). AutoSAS shows state-of-the-art performance and achieves better results by over 8% in some of the question prompts as measured by Quadratic Weighted Kappa (QWK), showing performance comparable to humans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
