%0 Journal Article %A HUANG Wei %A LIU Guiquan %T Study on Hierarchical Multi-Label Text Classification Method of MSML-BERT Model %D 2022 %R 10.3778/j.issn.1002-8331.2111-0176 %J Computer Engineering and Applications %P 191-201 %V 58 %N 15 %X Hierarchical multi-label text classification is more challenging than ordinary multi-label text classification, since multiple labels of the text establish a tree-like hierarchy. Current methods use the same model structure to predict labels at different layers, ignoring their differences and diversity. They don’t model the hierarchical dependencies fully, resulting in poor prediction performance of labels at all layers, especially the lower-layer long-tail labels, and may lead to label inconsistency problems. In order to address the above problems, the multi-task learning architecture is introduced, and the MSML-BERT model is proposed. The model regards the label classification network of each layer in the label hierarchy as a learning task, and enhances the performance of tasks at all layers through the sharing and transfer of knowledge between tasks. Based on this, a multi-scale feature extraction module is designed to capture multi-scale and multi-grained features to form various knowledge required at different layers. Further, a multi-layer information propagation module is designed to fully model hierarchical dependencies and transfer knowledge in different layers to support lower-layer tasks. In this module, a hierarchical gating mechanism is designed to filter the knowledge flow among tasks in different layers. Extensive experiments are conducted on the RCV1-V2, NYT and WOS datasets, and the results reveal that the entire performance of this model, especially on the lower-layer long-tail labels, surpasses that of other prevailing models and maintains a low label inconsistency ratio. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2111-0176