Using Very Deep Convolutional Neural Networks to Automatically Detect Plagiarized Spoken Responses

This study focuses on the automatic plagiarism detection in the context of high-stakes spoken language proficiency assessment, in which some test takers may attempt to game the test by memorizing prepared source materials before the test and then adapting them on-the-fly during the test to produce their spoken responses. When trying to identify such instances of plagiarism, experienced human raters attempt to find salient matching expressions that appear both in potential source materials and the test responses. This motivates an approach that visualizes a grid of lexical matches between a test response and a source and then applies state-of-the-art image recognition techniques to detect patterns of matching sequences. This study employs Inception networks-very deep convolutional neural networks-to build automatic detection models. The system achieves an F1-score of 79.6% on the class of plagiarized responses outperforming a baseline system based on word sequence matching (F1-score of 74.1%).