A semantic representation model incorporating knowledge point information for test question duplication detection

Abstract

The repetition of test questions raises a significant challenge to test fairness. When processing the test texts, we found that the repetition of test questions is not only the similarity of context but also the context difference with the similar semantics, which is more challenging to identify. In addition, the existence of formulas in the test questions brings a greater challenge to detect the repetition of test questions. To address the above problems, we propose a semantic embedding representation model for test question duplication detection. The model incorporates knowledge point information and extracts the semantic information of test questions through a dual encoder structure and set a threshold to obtain top-n test questions with high similarity by calculating the cosine similarity between test questions. The experimental results show that this paper's proposed model greatly improved over the traditional repetitive detection model.