## 5 Discussion  
The application of optimization techniques or hybrid approaches leads to notable increases in model performance when compared to the baseline model accuracies. A thorough discussion and comparison are provided below. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Original Sentence** & **True Tense** & **Predicted Sense** & **Correct** \\ \hline
0 & Ke ile ka supa gore ga bo Mahlatse & Location & Time & False \\  & ke kae. & & & \\ \hline
1 & Bomma bana le bana ba supa dintlo & Number seven & Number seven & True \\  & tsa boste. & & & \\ \hline
2 & Mmae o rile ketswa kae ka iri ya & time & Time & True \\  & bo supa. & & & \\ \hline
3 & Maabane nna le Mogwera waka re & Number seven & Number seven & True \\  & rekile dipene tse supa. & & & \\ \hline
4 & Bo koko ba rile ke se ka supa batho ke & Location & Location & True \\  & sa ba tsebe. & & & \\ \hline
5 & Ngwana wa geso o na le mengwaga & Number seven & Number seven & True \\  & ye supa. & & & \\ \hline
6 & Rakgolo o rile ge o bala dikgom o & Location & Location & True \\  & bale ka go di supra. & & & \\ \hline
7 & Ka gae re na le di kates tse supa. & Number seven & Number seven & True \\ \hline
8 & Ke ile ka supa gore sekolo saka & Location & See & False \\  & ke safe. & & & \\ \hline
9 & Ke na le maeba a supa. & Number seven & Number seven & True \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Predicted sense. The baseline results in Table 7 indicate that BiGRU's efficient and context-aware bidirectional architecture makes it the most effective standalone model. BiGRU (optimized with ADAM): This optimizer increases training efficiency and gradient updates, which results in a 5% increase in accuracy over the standard BiGRU, as seen in Tables 7 and 8. Emphasizing the value of focusing on pertinent portions of the input sequence, attention methods are introduced to further improve accuracy. The performances of the hybrid models BiGRU + DeBERTa (85%) and BiGRU + ALBERT (84%) are shown in Table 8:  
* These hybrids achieve the highest ratings by combining the potent context modeling of transformer-based architectures with the bidirectional efficiency of BiGRU. * Hybrid BiGRU + BERT (79%): its performance is comparable to the basic BiGRU, indicating either little additional advantage or inadequate hybrid tuning. * Hybrid BiGRU + RoBERTa (70%): the outcome is disappointing, which could be the result of difficulties in integrating the hybrid model or the less-than-ideal architecture synergy. enhance contextual understanding in polysemous word disambiguation. The Hybrid BiGRU + DeBERTa model (85%) performs equally to BiGRU + attention, suggesting that DeBERTa's improved encoding of syntactic dependencies benefits WSD in Sesotho sa Leboa. However, Hybrid BiGRU + BERT (79%) and Hybrid BiGRU + RoBERTa (70%) show lower accuracy, likely due to BERT and RoBERTa's suboptimal generalization for low-resource linguistic structures. When compared to other languages in Table 9, Amharic (BiGRU: 99.99%) and Ge'ez (99.52% using multiple classifiers) significantly outperform Sesotho sa Leboa models, likely due to larger annotated datasets and optimized architectures. Portuguese (BERT: 84%) achieves similar accuracy to BiGRU-based models in Sesotho sa Leboa, while Korean (Viterbi Algorithm: 76.4%) and Hindi (Word2 Vec: 58%) perform notably lower, reinforcing the importance of contextual embeddings and neural architectures for effective WSD. Amharic's joint supervised and unsupervised approach (86% accuracy, 92.5% F1-score) suggests that semi-supervised methods could enhance Sesotho sa Leboa's WSD performance. Meanwhile, Arabic's BERT model (96% F1-score) further highlights the benefits of transformer-based architectures in high-resource settings. Overall, while Sesotho sa Leboa's BiGRU-based models achieve robust performance, they still lag behind top-performing WSD models for Amharic and Ge'ez, indicating a need for larger annotated corpora, improved hybrid architectures, and semi-supervised learning strategies.