In recent advancements in natural language processing, the fusion of deep learning architectures has shown promising improvements in machine reading comprehension and question answering tasks. This study proposes a hybrid model combining the Bidirectional Encoder Representations from Transformers (BERT) with Long Short-Term Memory (LSTM) networks to leverage both context-aware embeddings generated by BERT and the sequence learning capabilities of LSTM. We utilize the Stanford Question Answering Dataset (SQuAD) as our primary dataset, which provides a robust benchmark for training and evaluating our model. The model architecture is designed to first use BERT to encode contextual relationships within the text, and then pass these embeddings to an LSTM layer that processes the sequence to predict the answer spans. Evaluation metrics, including loss and the F1 score, are used to quantify the model's performance, aiming to optimize both precision and recall of the answer predictions. Our results indicate that the hybrid model achieves superior performance compared to using each individual component alone, demonstrating the effectiveness of combining BERT with LSTM for complex question answering tasks in natural language understanding.

Keywords: Machine Reading Comprehension, Question Answering, Hybrid Model, BERT (Bidirectional Encoder Representations from Transformers), LSTM (Long Short-Term Memory), SQuAD (Stanford Question Answering Dataset), Natural Language Processing, Deep Learning, Contextual Embeddings, Sequence Learning, Model Evaluation, F1 Score, Precision and Recall, Text Understanding