Hierarchy-Aware Global Model for Hierarchical Text Classification

Jie Zhou, Chunping Ma, Dingkun Long, Guangwei Xu, Ning Ding, Haoyu Zhang, Pengjun Xie, Gongshen Liu


Abstract
Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy. Existing methods have difficulties in modeling the hierarchical label structure in a global view. Furthermore, they cannot make full use of the mutual interactions between the text feature space and the label space. In this paper, we formulate the hierarchy as a directed graph and introduce hierarchy-aware structure encoders for modeling label dependencies. Based on the hierarchy encoder, we propose a novel end-to-end hierarchy-aware global model (HiAGM) with two variants. A multi-label attention variant (HiAGM-LA) learns hierarchy-aware label embeddings through the hierarchy encoder and conducts inductive fusion of label-aware text features. A text feature propagation model (HiAGM-TP) is proposed as the deductive variant that directly feeds text features into hierarchy encoders. Compared with previous works, both HiAGM-LA and HiAGM-TP achieve significant and consistent improvements on three benchmark datasets.
Anthology ID:
2020.acl-main.104
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1106–1117
Language:
URL:
https://aclanthology.org/2020.acl-main.104
DOI:
10.18653/v1/2020.acl-main.104
Bibkey:
Cite (ACL):
Jie Zhou, Chunping Ma, Dingkun Long, Guangwei Xu, Ning Ding, Haoyu Zhang, Pengjun Xie, and Gongshen Liu. 2020. Hierarchy-Aware Global Model for Hierarchical Text Classification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1106–1117, Online. Association for Computational Linguistics.
Cite (Informal):
Hierarchy-Aware Global Model for Hierarchical Text Classification (Zhou et al., ACL 2020)
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PDF:
https://preview.aclanthology.org/paclic-22-ingestion/2020.acl-main.104.pdf
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 http://slideslive.com/38929004