  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.
