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.
2016 IEEE Spoken Language Technology Workshop (SLT)