Constrained Sequence-to-Tree Generation for Hierarchical Text Classification
Chao Yu, Yi Shen, Yue Mao · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
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.