# A Dual-Directional Context-Aware Test-Time Learning for Text Classification

arXiv: https://arxiv.org/abs/2503.15469 · HTML: https://mlsanity.com/paper/2503.15469/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Joint Embedding of Words and Labels for Text Classification](https://mlsanity.com/paper/1805.04174/) (2018)
- [Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms](https://mlsanity.com/paper/1805.09843/) (2018)
- [Investigating Capsule Networks with Dynamic Routing for Text Classification](https://mlsanity.com/paper/1804.00538/) (2018)
- [On Tree-Based Neural Sentence Modeling](https://mlsanity.com/paper/1808.09644/) (2018)
- [Explicit Interaction Model towards Text Classification](https://mlsanity.com/paper/1811.09386/) (2018)
- [Compositional Coding Capsule Network with K-Means Routing for Text Classification](https://mlsanity.com/paper/1810.09177/) (2018)
- [Squeezed Very Deep Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/1901.09821/) (2019)
- [Abstractive Text Classification Using Sequence-to-convolution Neural Networks](https://mlsanity.com/paper/1805.07745/) (2018)

## Built on by (most cited first, 1 of 1)

- [Mechanistic Interpretability of Fine-Tuned Vision Transformers on Distorted Images: Decoding Attention Head Behavior for Transparent and Trustworthy AI](https://mlsanity.com/paper/2503.18762/) (2025)

## Abstract (arXiv)

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.