App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 8

ALU-TransSHAP: unbiased TransSHAP explainable model based on active learning

Liu Tong
Yang Yaxuan
Ni Weijian
School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao Shandong 266590, China
DOI: 10.19734/j.issn.1001-3695.2025.12.0507

Abstract

With the widespread application of deep learning models in sentiment analysis, the "black-box" nature induces problems like opaque prediction processes and difficult tracing of key decision-making factors, which seriously impairs model credibility and practical deployment. This study proposes an Active Learning-based Unbiased TransSHAP Explanation Model (ALU-TransSHAP). The model screens high-entropy samples via an active learning background selection module to mitigate the distribution bias of background data, optimizes the TransSHAP adaptation layer to construct a bidirectional mapping between short sentences and subwords for aligning semantic units with explanation units, and designs an unbiased Shapley value calculation engine integrated with paired sampling to enhance the accuracy and stability of feature attribution. Experiments on the Weibo Hot Major Review Dataset and SENTI_RATIONALE Dataset demonstrate that ALU-TransSHAP outperforms all baseline models significantly in fidelity (0.89/0.87), stability (0.92/0.88) and prediction change (0.83/0.92), while maintaining acceptable sparsity (0.209/0.211) and computational efficiency (2.5/2.7). The proposed ALU-TransSHAP effectively addresses the interpretation challenges of Transformer models in Chinese scenarios, fully preserves semantic logic, provides reliable explanatory support for sentiment analysis, and remarkably improves the transparency and credibility of model decision-making.

Foundation Support

山东省自然科学基金资质项目(ZR2022MF319)
科技创新2030—"新一代人工智能"重大项目(2022ZD0119502-07)
新一代人工智能国家科技重大专项(2022ZD0119501)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.12.0507
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 8

Publish History

[2026-04-22] Accepted Paper

Cite This Article

刘彤, 杨雅萱, 倪维健. ALU-TransSHAP:基于主动学习的无偏TransSHAP可解释模型 [J]. 计算机应用研究, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0507. (Liu Tong, Yang Yaxuan, Ni Weijian. ALU-TransSHAP: unbiased TransSHAP explainable model based on active learning [J]. Application Research of Computers, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0507. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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