## 4 Results
### Comparison with Other Studies  
In comparison to other studies, our approach demonstrates significant improvements in key translation metrics. As noted in Table 9, while previous studies, such as those by [PERSON] et al. (2019) [39], [PERSON] et al. (2017) [40], and [PERSON] et al. (2016) [41], have made notable contributions to various machine translation techniques, our study excels in SpBLEU, ROUGE, and WER, outperforming these works in translation accuracy, content preservation, and structural alignment. Specifically, our fine-tuned model achieves a 71.30 SpBLEU score, significantly higher than the other studies, indicating superior sentence-level translation. Additionally, our approach's use of cross-lingual learning and dynamic dataset aggregation has led to more efficient utilization of high-resource language data, thereby enhancing performance for low-resource languages like Twi. While further improvements are still needed for rare words and phrases, our model's performance across these evaluation metrics highlights the effectiveness of our proposed methods in the context of low-resource language translation.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Input Sentence** & **Fine-Tuned Translation** & **Reference Translation** & **Observed Error** \\ \hline The cat is under the table. & \(\varepsilon\)noma no wó sika no ase. & Pón\(\varepsilon\) no wó pono no ase. & Wrong subject used. \\ \hline I will eat tomorrow. & Me bcy\(\varepsilon\) addiid nnc. & Me bcy\(\varepsilon\) addiid ikyena. & Wrong tense used. \\ \hline Where is the teacher? & Wó he na mpqtam sika w? & Éhe na kyer\(\varepsilon\)kyer\(\varepsilon\)foo no & Unnatural phrasing for \"teacher\". \\ \hline This book is interesting. & \(\varepsilon\)h\(\alpha\)nne\(\varepsilon\)ma no y\(\varepsilon\) fef\(\varepsilon\)cf\(\varepsilon\). & Abakos\(\varepsilon\)m yi y\(\varepsilon\) anika & Overgeneralized \\  & & dodo. & vocabulary. \\ \hline The child is running fast. & Abófa no retu kwan dada. & Abófa no retu kwan nt\(\varepsilon\) & Missing adverb translation. \\ \hline \hline \end{tabular}
\end{table}
Table 8: Examples of errors in translation.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Study** & **Methodology/Approach** & **SpBLEU** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** & **WER** & **Limitations** \\ \hline [PERSON] et al., & The study introduces a & & & & & & The model’s \\
2019 [39] & The study introduces a & & & & & & performance \\  & neural summarization & & & & & & heavily depends \\  & model that customizes & & & & & on user input for \\ [PERSON] et al., & output based on user & & & & & & optimal summary \\
2019 [39] & preferences for length, & & & & & & customization. \\  & style, and entities, with & & & & & & \\  & default settings applied & & & & & & \\  & when no input is given. & & & & & & \\ \hline  & Introduces a neural & & & & & & Reinforcement \\  & network model with & & & & & learning-based \\  & intra-attention and & & & & & & training is \\  & combines supervised & & & & & & computationally \\
2017 [40] & word prediction with & N/A & 38.30 & 14.81 & 35.49 & N/A & \\  & reinforcement learning & & & & & & intensity and \\  & (RL) to improve & & & & & & complex. \\  & summarization quality. & & & & & & \\ \hline  & Uses Attentional & & & & & & struggle with \\  & Encoder–Decoder RNNs & & & & & accurately \\  & for abstractive & & & & & generating rare or \\
2016 [41] & summarization, & & & & & & highly specialized \\  & improving keyword & & & & & & words in certain \\  & modeling and rare & & & & & & contexts. \\  & word handling. & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison with existing studies.