In this paper, we explore unsupervised techniques for the task of automatic short answer grading. We compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. Overall, our system significantly and consistently outperforms other unsupervised methods for short answer grading that have been proposed in the past.
The authors in REF investigated the application of knowledge-based and corpus-based measures of text similarity for automatic short answer grading.
Text-to-Text Semantic Similarity for Automatic Short Answer Grading
EACL
Computer Science