Text-to-Text Semantic Similarity for Automatic Short Answer Grading

By Michael Mohler, Rada Mihalcea. Conference of the European Chapter of the Association for Computational Linguistics, 2009

353 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0f0141cfd1bc89f8ed78a91d4894ade576056571

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Citations in the corpus

The Eras and Trends of Automatic Short Answer Grading (2015, background): “Apart from comparing these measures, (Mohler and Mihalcea 2009) also consider incorporating the best student answers with the teacher answer to expand the teacher answer vocabulary, which they find to be effective.”
An automated essay scoring systems: a systematic literature review (2021, background): “Mohler and Mihalcea (2009). proposed text-to-text semantic similarity to assign a score to the student essays.”
Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments (2011, methodology): “A text similarity approach was taken in (Mohler and Mihalcea, 2009), where a grade is assigned based on a measure of relatedness between the student and the instructor answer.”