## 5 Discussions and Contributions
### Application Beyond Twi: Potential for Other Low-Resource Languages  
The practical implications of our work are far-reaching. By addressing the challenge of machine translation for underrepresented languages, particularly African languages like Twi, our model has the potential to significantly improve translation services and bridge communication gaps, especially in regions with large non-English-speaking populations. This can have wide-ranging applications across international communication, education,health services, and governance. Additionally, the cross-lingual transfer learning method we employed, which leverages high-resource language data, can be extended to other language pairs, making it applicable in situations where data disparity exists between the languages being translated.  
This framework can also be adapted to a variety of low-resource languages, particularly those in Africa, Asia, and Latin America, contributing to the development of scalable multilingual NLP technologies. Beyond translation, the fine-tuned model can be integrated into multilingual chatbots and virtual assistants, significantly enhancing their ability to communicate in low-resource languages. This would broaden the reach and functionality of such systems in diverse linguistic contexts. The model could also be utilized in automated document translation systems, making documents more accessible to non-English speakers in critical sectors such as legal, healthcare, and governance, thereby improving communication and service delivery in multilingual communities.  
The implications of this research extend to several audiences. For the research community, our work introduces a new approach to improving machine translation for underrepresented languages, opening avenues for further exploration in cross-lingual learning and data augmentation techniques. Researchers in federated learning and NLP can gain valuable insights on how to fine-tune models for resource-scar languages using high-resource datasets. For policymakers and governments, especially those working in multilingual contexts, our advancements provide the potential to enhance communication and service delivery to speakers of low-resource languages. Educational institutions in multilingual countries can also benefit, as this research could help provide more equitable access to educational materials through improved machine translation systems. The tech industry can apply these findings to enhance AI-driven language translation tools, improving inclusivity and global accessibility in fields such as healthcare, business, and customer service.  
Beyond its technical impact, this research holds significant societal implications, particularly in cultural preservation and digital inclusivity. Many low-resource languages, such as Twi, encapsulate rich cultural histories, oral traditions, and indigenous knowledge, yet they remain largely underrepresented in digital and technological spaces. By improving machine translation for these languages, our approach helps bridge the digital divide, ensuring that speakers of underrepresented languages can actively participate in online discourse. This fosters linguistic identity, educational empowerment, and access to essential information in healthcare, governance, and legal sectors. Furthermore, enhanced NLP capabilities for such languages support economic inclusion, enabling businesses and public institutions to communicate effectively with diverse linguistic communities. As digital transformation continues to shape global interactions, ensuring linguistic inclusivity will be crucial for building a more equitable technological landscape.  
This study represents a significant contribution to the field of low-resource language processing, demonstrating how contemporary NLP techniques can be leveraged to improve translation systems for underrepresented languages. Its broad implications for real-world applications and future research further highlight its importance in advancing both technology and social equity.