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View all- Wang GDu YJiang YLiu JLi XChen XGao HXie CLee Y(2024)Label-text bi-attention capsule networks model for multi-label text classificationNeurocomputing10.1016/j.neucom.2024.127671588:COnline publication date: 17-Jul-2024
A Dual-branch Learning Model with Gradient-balanced Loss for Long-tailed Multi-label Text Classification
Article No.: 34, Pages 1 - 24
Abstract
Multi-label text classification has a wide range of applications in the real world. However, the data distribution in the real world is often imbalanced, which leads to serious long-tailed problems. For multi-label classification, due to the vast scale of datasets and existence of label co-occurrence, how to effectively improve the prediction accuracy of tail labels without degrading the overall precision becomes an important challenge. To address this issue, we propose A Dual-Branch Learning Model with Gradient-Balanced Loss (DBGB) based on the paradigm of existing pre-trained multi-label classification SOTA models. Our model consists of two main long-tailed module improvements. First, with the shared text representation, the dual-classifier is leveraged to process two kinds of label distributions; one is the original data distribution and the other is the under-sampling distribution for head labels to strengthen the prediction for tail labels. Second, the proposed gradient-balanced loss can adaptively suppress the negative gradient accumulation problem related to labels, especially tail labels. We perform extensive experiments on three multi-label text classification datasets. The results show that the proposed method achieves competitive performance on overall prediction results compared to the state-of-the-art methods in solving the multi-label classification, with significant improvement on tail-label accuracy.
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Index Terms
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Publication History
Published: 27 September 2023
Online AM: 04 August 2023
Accepted: 28 April 2023
Revised: 26 February 2023
Received: 12 May 2022
Published in TOIS Volume 42, Issue 2
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- TJU-Wenge joint laboratory funding, Tianjin Research Innovation Project for Postgraduate Students
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View all- Wang GDu YJiang YLiu JLi XChen XGao HXie CLee Y(2024)Label-text bi-attention capsule networks model for multi-label text classificationNeurocomputing10.1016/j.neucom.2024.127671588:COnline publication date: 17-Jul-2024
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