A Dual-Directional Context-Aware Test-Time Learning for Text Classification
This work addresses text classification challenges for researchers and practitioners, but it appears incremental as it builds on existing deep learning methods.
The authors tackled the problem of text classification by addressing trade-offs in interpretability, efficiency, and contextual range, resulting in the Dynamic Bidirectional Elman Attention Network (DBEAN) that dynamically weights critical input segments while preserving computational efficiency.
Text classification assigns text to predefined categories. Traditional methods struggle with complex structures and long-range dependencies. Deep learning with recurrent neural networks and Transformer models has improved feature extraction and context awareness. However, these models still trade off interpretability, efficiency and contextual range. We propose the Dynamic Bidirectional Elman Attention Network (DBEAN). DBEAN combines bidirectional temporal modeling and self-attention. It dynamically weights critical input segments and preserves computational efficiency.