Work

MDCNN: Multi-Teacher Distillation-Based CNN for News Text Classification

Year: 2025

Type: article

Abstract: News text classification is crucial for efficient information acquisition and dissemination. While deep learning models, such as BERT and BiGRU, excel in accuracy for text classification, their high complexity and resource demands hinder practical deployment. To address these challenges, we proposed MDCNN (Multi-teacher Distillation-based CNN), which leverages knowledge distillation with BERT and BiGRU as teacher models and TextCNN as the student model. Experiments on three benchmark news dataset demonstrate that MDCNN improves classification accuracy by nearly 2% while significantly reducing computational overhead, offering a practical solution for real-world applications.

Source: IEEE Access

DOI: 10.1109/access.2025.3555224

Authors: Xiaolei Guo, Qingyang Liu, Yanrong Hu, Hongjiu Liu

Institutions: Zhejiang A & F University, University of Göttingen

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: Science Foundation of Ministry of Education of China