Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms
This addresses the challenge for academicians in efficiently managing question databases in online learning, though it is incremental as it builds on existing unsupervised and hybrid methods.
The paper tackles the problem of manually detecting duplicate and related questions in e-learning platforms by proposing QDup, an unsupervised tool that uses a hybrid pipeline to achieve high accuracy and speed in identifying near-duplicates and suggesting related questions from large repositories.
Online learning platforms provide diverse questions to gauge the learners' understanding of different concepts. The repository of questions has to be constantly updated to ensure a diverse pool of questions to conduct assessments for learners. However, it is impossible for the academician to manually skim through the large repository of questions to check for duplicates when onboarding new questions from external sources. Hence, we propose a tool QDup in this paper that can surface near-duplicate and semantically related questions without any supervised data. The proposed tool follows an unsupervised hybrid pipeline of statistical and neural approaches for incorporating different nuances in similarity for the task of question duplicate detection. We demonstrate that QDup can detect near-duplicate questions and also suggest related questions for practice with remarkable accuracy and speed from a large repository of questions. The demo video of the tool can be found at https://www.youtube.com/watch?v=loh0_-7XLW4.