  Online learning systems have multiple data repositories in the form of
transcripts, books and questions. To enable ease of access, such systems
organize the content according to a well defined taxonomy of hierarchical
nature (subject-chapter-topic). The task of categorizing inputs to the
hierarchical labels is usually cast as a flat multi-class classification
problem. Such approaches ignore the semantic relatedness between the terms in
the input and the tokens in the hierarchical labels. Alternate approaches also
suffer from class imbalance when they only consider leaf level nodes as labels.
To tackle the issues, we formulate the task as a dense retrieval problem to
retrieve the appropriate hierarchical labels for each content. In this paper,
we deal with categorizing questions. We model the hierarchical labels as a
composition of their tokens and use an efficient cross-attention mechanism to
fuse the information with the term representations of the content. We also
propose an adaptive in-batch hard negative sampling approach which samples
better negatives as the training progresses. We demonstrate that the proposed
approach \textit{TagRec++} outperforms existing state-of-the-art approaches on
question datasets as measured by Recall@k. In addition, we demonstrate
zero-shot capabilities of \textit{TagRec++} and ability to adapt to label
changes.
