Improving Response Prediction: Transformer Integration and Optimizer Evaluation

Our research paper focuses on utilizing the SR_BERT and SR_ALBERT approaches for the answer selection task within a question-answer system. The study initiates with data preprocessing from the SQuAD2.0 dataset, involving question and context pairs, followed by data analysis. The SR_BERT and SR_ALBERT approaches are employed to encode the data by combining the outputs of BERT LARGE and ALBERT LARGE models. Subsequently, a dense classification layer with a softmax function is added to predict answers in terms of probabilities. To enhance prediction accuracy, a CNN classifier with a BiLSTM is integrated to refine response selection, while an ADAM optimizer is utilized for weight adjustment and optimization of the SR_BERT and SR_ALBERT approaches. Finally, the approaches are evaluated against hyperparameters and metrics, revealing that the SR_ALBERT approach outperforms the SR_BERT approach, achieving an accuracy of 89.96%, an accuracy rate of 81.60%, and an F1 score of 89.03%.