Automatic plagiarism detection for spoken responses in an assessment of English language proficiency

This paper addresses the task of automatically detecting plagiarized responses in the context of a test of spoken English proficiency for non-native speakers. Text-to-text content similarity features are used jointly with speaking proficiency features extracted using an automated speech scoring system to train classifiers to distinguish between plagiarized and non-plagiarized spoken responses. A large data set drawn from an operational English proficiency assessment is used to simulate the performance of the detection system in a practical application. The best classifier on this heavily imbalanced data set resulted in an F1-score of 0.706 on the plagiarized class. These results indicate that the proposed system can potentially be used to improve the validity of both human and automated assessment of non-native spoken English.