## 4 Results
### Comparative Analysis of Model Accuracies in WSD
Better performance and faster convergence are usually achieved when using the Adam optimizer in conjunction with BiGRU (Bidirectional Gated Recurrent Unit), particularly when non-linear optimization and huge datasets are involved. The improved performance (84%) demonstrates how well Adam optimization works to increase convergence and final model performance, demonstrating that optimizing conventional RNN-based models, such as BiGRU, can result in considerable gains. BiGRU's learning of significant patterns, particularly in lengthy sequences, is enhanced by the addition of an attention mechanism, which helps the model focus on particular segments of the input sequence. Among the models that use BiGRU, the accuracy of 85% is the highest, demonstrating the significance of attention mechanisms in identifying subtle patterns and enhancing the overall accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model 1** & **Model 2** & **Statistic** & \(p\)**-Value** & **Confidence Interval** \\ \hline BiGRU (ADAM) & BiGRU (Attention) & 127.000 & 0.22774 & \begin{tabular}{} (0.4792592363193085, \\ 0.597104400044328) \\ \end{tabular} \\ BiGRU (ADAM) & Hybrid BiGRU + BERT & 141.000 & 0.13899 & \begin{tabular}{} (0.4007746830967713, \\ 0.5118466761265298) \\ \end{tabular} \\ BiGRU (ADAM) & Hybrid BiGRU + RoBERTa & 121.000 & 0.00000 & \begin{tabular}{} (0.27236275494207975, \\ 0.3661956725003535) \\ \end{tabular} \\ BiGRU (ADAM) & Hybrid BiGRU + DeBERTa & 117.000 & 0.13573 & \begin{tabular}{} (0.48765376745025135, \\ 0.6088713290748452) \\ \end{tabular} \\ BiGRU (ADAM) & Hybrid BiGRU + ALBERT & 134.000 & 0.28799 & \begin{tabular}{} (0.47538143894824697, \\ 0.5908206516440875) \\ \end{tabular} \\ BiGRU Attention) & Hybrid BiGRU + BERT & 125.000 & 0.00638 & \begin{tabular}{} (0.3634355216212898, \\ 0.4754906528753545) \\ \end{tabular} \\ BiGRU Attention) & Hybrid BiGRU + RoBERTa & 112.000 & 0.00000 & \begin{tabular}{} (0.24754338530870543, \\ 0.3388440492462683) \\ \end{tabular} \\ BiGRU (Attention) & Hybrid BiGRU + DeBERTa & 133.000 & 0.85518 & \begin{tabular}{} (0.4477741478105329, \\ 0.5670406670040282) \\ \end{tabular} \\ BiGRU (Attention) & Hybrid BiGRU + ALBERT & 129.000 & 0.95056 & \begin{tabular}{} (0.43357974112738154, \\ 0.55692795118031081081) \\ \end{tabular} \\ Hybrid BiGRU + BERT & Hybrid BiGRU + RoBERTa & 150.000 & 0.00000 & \begin{tabular}{} (0.31923018679206183, \\ 0.4124771302811089) \\ \end{tabular} \\ Hybrid BiGRU + BERT & Hybrid BiGRU + DeBERTa & 117.000 & 0.00250 & \begin{tabular}{} (0.5339274010633905, \\ 0.6478907807547913) \\ \end{tabular} \\ Hybrid BiGRU + BERT & Hybrid BiGRU + ALBERT & 131.000 & 0.01023 & \begin{tabular}{} (0.5194619493666492, \\ 0.6298886999840001) \\ \end{tabular} \\ Hybrid BiGRU + RoBERTa & Hybrid BiGRU + DeBERTa & 106.000 & 0.00000 & \begin{tabular}{} (0.6709043846204781, \\ 0.7622506955934256) \\ \end{tabular} \\ Hybrid BiGRU + RoBERTa & Hybrid BiGRU + ALBERT & 103.000 & 0.00000 & \
\begin{tabular}{} (0.6689909392452787, \\ 0.7619482872740583) \\ \end{tabular} \\ Hybrid BiGRU + DeBERTa & Hybrid BiGRU + ALBERT & 135.000 & 0.76350 &
\begin{tabular}{} (0.43015639091530755, \\ 0.5481044786499099) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Contingency table for enhanced models. BiGRU and BERT, pre-trained transformer models renowned for their potent contextual language understanding, are combined into a hybrid model. Utilizing the advantages of both models is the goal of the hybrid method. Although the hybrid BiGRU + BERT's accuracy of 79% is respectable, it raises the possibility that the combination may not fully utilize each component's advantages. Performance could be further enhanced by adjusting the model or implementing a more effective integration method. A kind of BERT that optimizes training methods for improved performance is called RoBERTa (robustly optimized  
Figure 10: Comparative analysis of enhanced WSD models.