Paper Contents
Abstract
Transformer-Augmented LSTMs for Long-Term Dependency Learning is a deep learning hybrid model created to enhance long-range dependency retention in sequential data. It addresses LSTM's ability to learn sequences and Transformers self-attention to pick up local and global dependencies. It augments NLP tasks like text prediction, machine translation, and sentiment analysis by enhancing contextual understanding. The methods entail data pre-processing, feature extraction, hybrid model incorporation, and hyper parameter optimization. Attention-based improvements enhance sequence-to-sequence learning, and visualization methods such as loss curves and attention heat-maps offer insights. The method targets improved generalization and robustness, hence it is a scalable solution for NLP tasks.
Copyright
Copyright © 2025 S. Venkatesh. This is an open access article distributed under the Creative Commons Attribution License.
