  Text classification in education, usually called auto-tagging, is the
automated process of assigning relevant tags to educational content, such as
questions and textbooks. However, auto-tagging suffers from a data scarcity
problem, which stems from two major challenges: 1) it possesses a large tag
space and 2) it is multi-label. Though a retrieval approach is reportedly good
at low-resource scenarios, there have been fewer efforts to directly address
the data scarcity problem. To mitigate these issues, here we propose a novel
retrieval approach CEAA that provides effective learning in educational text
classification. Our main contributions are as follows: 1) we leverage transfer
learning from question-answering datasets, and 2) we propose a simple but
effective data augmentation method introducing cross-encoder style texts to a
bi-encoder architecture for more efficient inference. An extensive set of
experiments shows that our proposed method is effective in multi-label
scenarios and low-resource tags compared to state-of-the-art models.
