Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer

TL;DR

This paper introduces an enhanced Transformer model with multi-level attention and contrastive learning to improve text classification accuracy and efficiency, demonstrating superior performance over existing models on benchmark datasets.

Contribution

It proposes a novel multi-level attention mechanism combined with contrastive learning and a lightweight module for optimized text classification with improved semantic understanding.

Findings

Outperforms BiLSTM, CNN, Transformer, and BERT in accuracy, F1 score, and recall

Enhances semantic representation and generalization in text classification

Reduces computational cost with a lightweight feature transformation module

Abstract

This paper studies a text classification algorithm based on an improved Transformer to improve the performance and efficiency of the model in text classification tasks. Aiming at the shortcomings of the traditional Transformer model in capturing deep semantic relationships and optimizing computational complexity, this paper introduces a multi-level attention mechanism and a contrastive learning strategy. The multi-level attention mechanism effectively models the global semantics and local features in the text by combining global attention with local attention; the contrastive learning strategy enhances the model's ability to distinguish between different categories by constructing positive and negative sample pairs while improving the classification effect. In addition, in order to improve the training and inference efficiency of the model on large-scale text data, this paper designs a lightweight module to optimize the feature transformation process and reduce the computational cost. Experimental results on the dataset show that the improved Transformer model outperforms the comparative models such as BiLSTM, CNN, standard Transformer, and BERT in terms of classification accuracy, F1 score, and recall rate, showing stronger semantic representation ability and generalization performance. The method proposed in this paper provides a new idea for algorithm optimization in the field of text classification and has good application potential and practical value. Future work will focus on studying the performance of this model in multi-category imbalanced datasets and cross-domain tasks and explore the integration wi