Hierarchy-Aware Global Model for Hierarchical Text Classification

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1106–1117 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 1106 Hierarchy-Aware Global Model for Hierarchical Text Classification Jie Zhou1,2∗, Chunping Ma2, Dingkun Long2, Guangwei Xu2, Ning Ding3, Haoyu Zhang4, Pengjun Xie2, Gongshen Liu1† 1School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University 2Alibaba Group, 3Tsinghua University, 4National University of Defense Technology {sanny02,lgshen}@sjtu.edu.cn, {kunka.xgw,chengchen.xpj}@taobao.com {chunping.mcp,dingkun.ldk}@alibaba-inc.com {dingn18}@mails.tsinghua.edu.cn, {zhanghaoyu10}@nudt.edu.cn Abstract Hierarchical text classification is an essential yet challenging subtask of multi-label text clas- sification with a taxonomic hierarchy. Existing methods have difficulties in modeling the hier- archical label structure in a global view. Fur- thermore, they cannot make full use of the mu- tual interactions between the text feature space and the label space. In this paper, we for- mulate 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 (Hi- AGM) with two variants. A multi-label at- tention variant (HiAGM-LA) learns hierarchy- aware label embeddings through the hierarchy encoder and conducts inductive fusion of label- aware text features. A text feature propaga- tion model (HiAGM-TP) is proposed as the de- ductive variant that directly feeds text features into hierarchy encoders. Compared with pre- vious works, both HiAGM-LA and HiAGM- TP achieve significant and consistent improve- ments on three benchmark datasets. 1 Introduction Text classification is widely used in Natural Lan- guage Processing (NLP) applications, such as sen- timental analysis (Pang and Lee, 2007), informa- tion retrieval (Liu et al., 2015), and document cat- egorization (Yang et al., 2016). Hierarchical text classification (HTC) is a particular multi-label text classification (MLC) problem, where the classifica- tion result corresponds to one or more nodes of a taxonomic hierarchy. The taxonomic hierarchy is commonly modeled as a tree or a directed acyclic graph, as depicted in Figure 1. Existing approaches for HTC could be catego- rized into two groups: local approach and global ∗This work was done during intern at Alibaba Group. †Corresponding author. Figure 1: This short sample is tagged with news, sports, football, features and books. Note that HTC could be either a single-path or a multi-path problem. approach. The first group tends to constructs mul- tiple classification models and then traverse the hierarchy in a top-down manner. Previous local studies (Wehrmann et al., 2018; Shimura et al., 2018; Banerjee et al., 2019) propose to overcome the data imbalance on child nodes by learning from parent one. However, these models contain a large number of parameters and easily lead to exposure bias for the lack of holistic structural information. The global approach treats HTC problem as a flat MLC problem, and uses one single classifier for all classes. Recent global methods introduce var- ious strategies to utilize structural information of top-down paths, such as recursive regularization (Gopal and Yang, 2013), reinforcement learning (Mao et al., 2019) and meta-learning (Wu et al., 2019). There is so far no global method …

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