  Recently, e-learning platforms have grown as a place where students can post
doubts (as a snap taken with smart phones) and get them resolved in minutes.
However, the significant increase in the number of student-posted doubts with
high variance in quality on these platforms not only presents challenges for
teachers' navigation to address them but also increases the resolution time per
doubt. Both are not acceptable, as high doubt resolution time hinders the
students learning progress. This necessitates ways to automatically identify if
there exists a similar doubt in repository and then serve it to the teacher as
the plausible solution to validate and communicate with the student. Supervised
learning techniques (like Siamese architecture) require labels to identify the
matches, which is not feasible as labels are scarce and expensive. In this
work, we, thus, developed a label-agnostic doubt matching paradigm based on the
representations learnt via self-supervised technique. Building on prior
theoretical insights of BYOL (bootstrap your own latent space), we propose
custom BYOL which combines domain-specific augmentation with contrastive
objective over a varied set of appropriately constructed data views. Results
highlighted that, custom BYOL improves the top-1 matching accuracy by
approximately 6\% and 5\% as compared to both BYOL and supervised learning
instances, respectively. We further show that both BYOL-based learning
instances performs either on par or better than human labeling.
