  Hierarchical Text Classification (HTC) is a challenging task where a document
can be assigned to multiple hierarchically structured categories within a
taxonomy. The majority of prior studies consider HTC as a flat multi-label
classification problem, which inevitably leads to "label inconsistency"
problem. In this paper, we formulate HTC as a sequence generation task and
introduce a sequence-to-tree framework (Seq2Tree) for modeling the hierarchical
label structure. Moreover, we design a constrained decoding strategy with
dynamic vocabulary to secure the label consistency of the results. Compared
with previous works, the proposed approach achieves significant and consistent
improvements on three benchmark datasets.
