## 6 Conclusions  
The experimental findings indicate that BiGRU models, whether standalone or hybrid, are successful in addressing word sense disambiguation (WSD) for Sesotho sa Leboa. The highest accuracy of 85% was achieved with BiGRU (Optimized with Attention) and Hybrid \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Language** & **Model** & **Accuracy** & **F1 Score** & **Authors** \\ \hline Amharic & BiGRU & 99.99 & 92.5 & [55] \\ \hline Amharic & Joint supervised, unsupervised & 86 & 83 & [22] \\ \hline Portuguese & BERT & 84 & & [58] \\ \hline Korean & Unsupervised (Viterbi algorithm) & 76.4 & 96 & [59] \\ \hline Arabic & BERT & & & [59] \\ \hline \multirow{3}{*}{Ge’ez} & Naive Bayes, decision trees, random forests, & & & \\  & K-nearest neighbor, logistic regression, & 99.52 & 92.5 & [43] \\ \cline{1-1}  & linear support vector machine & & & \\ \hline Hindi & Word2 Vec & 58 & 57 & [60] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Benchmarking table for WSD. BiGRU + DeBERTa, highlighting the importance of advanced transformer architectures and attention mechanisms in enhancing context representation. The model's disambiguation capabilities are augmented by the attention mechanism, enabling it to focus on significant portions of the input sequence. DeBERTa performs better at catching complex word meanings because of its improved spatial encoding and disentangled attention. Strong accuracy levels of 84% were maintained by BiGRU (Optimized with ADAM) and Hybrid BiGRU + ALBERT, demonstrating the value of ALBERT's parameter efficiency in capturing semantic subtleties and the efficacy of ADAM optimization for training stability. While ALBERT's factorized embedding parameterization maximizes model performance with fewer parameters, ADAM's adaptive learning rate aids in effective convergence. Although it performed well, the Hybrid BiGRU + BERT model's accuracy of 79% was marginally worse than that of its ALBERT and DeBERTa counterparts. The heavier architecture of BERT, which could not work as well with BiGRU, could be the cause of this. In this situation, BERT's enormous model size and conventional attention processes could add complexity without corresponding benefits. The lowest accuracy, 70%, was obtained by Hybrid BiGRU + RoBERTa, indicating that RoBERTa's pre-training dynamics or tokenization strategy might not be the best fit for MCD tasks in morphologically rich, low-resource languages. Combining RoBERTa's aggressive masking method with BiGRU's sequential processing may limit how successful it is. BiGRU's ability to recognize contextual dependencies--which are essential for deciphering polysemous words--is greatly enhanced by attention processes. Because of its novel attention mechanism and improvements in positional encoding, DeBERTa's design provides better integration with BiGRU. Parameter-efficient models like ALBERT maintain good performance with less computing complexity, while optimization techniques like ADAM help to increase the training efficiency. When addressing MCD in low-resource languages like Sesotho sa Leboa, the performance variation in hybrid models emphasizes the significance of model compatibility and the requirement for customized structures. These results imply that BiGRU-based models for WSD tasks in morphologically rich languages can be considerably improved by utilizing attention mechanisms and choosing appropriate transformer architectures. Conceptualization, M.A.M. and H.D.M. ; methodology, M.A.M. and H.D.M. ; formal analysis, M.A.M. and H.D.M. ; data curation, M.A.M. and H.D.M. ; writing--original draft preparation, M.A.M. and H.D.M. ; writing--review and editing, M.A.M. and H.D.M. ; visualization, M.A.M. and H.D.M. ; supervision, S.O.O., P.A.O. and F.G.; project administration, M.A.M. and H.D.M. ; funding acquisition, M.A.M. and H.D.M. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Research Foundation (NRF), grant number BAAP2204052075-PR-2023, through Sefako Makgatho Health Sciences University.