Constrained Sequence-to-Tree Generation for Hierarchical Text Classification

• URL: http://arxiv.org/abs/2204.00811v1
• Date: Sat, 2 Apr 2022 08:35:39 GMT
• Title: Constrained Sequence-to-Tree Generation for Hierarchical Text Classification
• Authors: Chao Yu, Yi Shen, Yue Mao, Longjun Cai
• Abstract summary: Hierarchical Text Classification (HTC) is a challenging task where a document can be assigned to multiple hierarchically structured categories within a taxonomy. 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.
• Score: 10.143177923523407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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