用于识别简答题中缺口的定向图对齐方法
Directed Graph-alignment Approach for Identification of Gaps in Short Answers
AI总结：
本文提出一种基于有向图对齐的方法，通过将学生答案与模型答案建模为有向图并进行比对，自动识别词、短语或句子级别的缺口，并在三个简答题评分数据集上验证了其有效性。
摘要：
在本文中，我们提出了一种通过将学生答案与相应的模型答案/参考答案进行自动比对来识别缺失项（称为缺口）的方法。缺口可以在词、短语或句子级别被识别出来。识别出的缺口有助于为学生的形成性评估提供反馈。缺口识别问题被建模为对表示给定问题的学生答案和相应模型答案的一对有向图进行对齐。为验证所提出的方法，我们基于简答题评分领域中三个广为人知的数据集——即北德克萨斯大学（UNT）、SciEntsBank和Beetle——开发了带有缺口标注的学生答案，该缺口标注学生答案数据集可在以下地址获取：https://github.com/sahuarchana7/gaps-answers-dataset。我们采用了传统机器学习任务中使用的评估指标来评估缺口识别任务。尽管所提出方法在不同数据集和答案类型上的表现有所差异，但总体而言，其性能表现是令人鼓舞的。
英文摘要：
In this paper, we have presented a method for identifying missing items known as gaps in the student answers by comparing them against the corresponding model answer/reference answers, automatically. The gaps can be identified at word, phrase or sentence level. The identified gaps are useful in providing feedback to the students for formative assessment. The problem of gap identification has been modelled as an alignment of a pair of directed graphs representing a student answer and the corresponding model answer for a given question. To validate the proposed approach, the gap annotated student answers considering answers from three widely known datasets in the short answer grading domain, namely, University of North Texas (UNT), SciEntsBank, and Beetle have been developed and this gap annotated student answers' dataset is available at: https://github.com/sahuarchana7/gaps-answers-dataset. Evaluation metrics used in the traditional machine learning tasks have been adopted to evaluate the task of gap identification. Though performance of the proposed approach varies across the datasets and the types of the answers, overall the performance is observed to be promising.
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。
