A Dual-branch Learning Model with Gradient-balanced Loss for Long-tailed Multi-label Text Classification

Author:

Yao Yitong1ORCID,Zhang Jing1ORCID,Zhang Peng1ORCID,Sun Yueheng1ORCID

Affiliation:

1. Tianjin University, China

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.

Funder

Natural Science Foundation of China

TJU-Wenge joint laboratory funding, Tianjin Research Innovation Project for Postgraduate Students

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,General Business, Management and Accounting,Information Systems

Reference53 articles.

1. Rohit Babbar and Bernhard Schölkopf. 2017. DiSMEC: Distributed sparse machines for extreme multi-label classification. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining. 721–729.

2. Tavor Z. Baharav, Daniel L. Jiang, Kedarnath Kolluri, Sujay Sanghavi, and Inderjit S. Dhillon. 2021. Enabling efficiency-precision trade-offs for label trees in extreme classification. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3717–3726.

3. K. Bhatia K. Dahiya H. Jain P. Kar A. Mittal Y. Prabhu and M. Varma. 2016. The extreme classification repository: Multi-label datasets and code. Retrieved from http://manikvarma.org/downloads/XC/XMLRepository.html

4. Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, and Prateek Jain. 2015. Sparse local embeddings for extreme multi-label classification. In Proceedings of the Annual Conference on Neural Information Processing Systems, Vol. 29. 730–738.

5. Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, and Tengyu Ma. 2019. Learning imbalanced datasets with label-distribution-aware margin loss. In Proceedings of the Annual Conference on Neural Information Processing Systems, Vol. 32. 1565–1576.

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