  Online educational platforms organize academic questions based on a
hierarchical learning taxonomy (subject-chapter-topic). Automatically tagging
new questions with existing taxonomy will help organize these questions into
different classes of hierarchical taxonomy so that they can be searched based
on the facets like chapter. This task can be formulated as a flat multi-class
classification problem. Usually, flat classification based methods ignore the
semantic relatedness between the terms in the hierarchical taxonomy and the
questions. Some traditional methods also suffer from the class imbalance issues
as they consider only the leaf nodes ignoring the hierarchy. Hence, we
formulate the problem as a similarity-based retrieval task where we optimize
the semantic relatedness between the taxonomy and the questions. We demonstrate
that our method helps to handle the unseen labels and hence can be used for
taxonomy tagging in the wild. In this method, we augment the question with its
corresponding answer to capture more semantic information and then align the
question-answer pair's contextualized embedding with the corresponding label
(taxonomy) vector representations. The representations are aligned by
fine-tuning a transformer based model with a loss function that is a
combination of the cosine similarity and hinge rank loss. The loss function
maximizes the similarity between the question-answer pair and the correct label
representations and minimizes the similarity to unrelated labels. Finally, we
perform experiments on two real-world datasets. We show that the proposed
learning method outperforms representations learned using the multi-class
classification method and other state of the art methods by 6% as measured by
Recall@k. We also demonstrate the performance of the proposed method on unseen
but related learning content like the learning objectives without re-training
the network.
