# Creating a Parallel Corpus for the Kazakh Sign Language and Learning
## 5 Discussion
This would allow for a more accurate representation of non-manual markers such as facial expressions and body movements, which are essential for conveying meaning in KSL. The evaluation metrics used in this study, particularly BLEU scores, offer a solid foundation for assessing translation quality. However, BLEU primarily focuses on \(n\)-gram overlap with reference translations, which may not fully capture the semantic depth and fluency needed for sign language translation. Future work could incorporate human evaluations or explore alternative metrics that prioritize meaning, context, and cultural relevance to provide a more comprehensive measure of translation quality. Looking ahead, this research sets the stage for further advancements in sign language technology. Beyond translation, the methodologies and findings have the potential to impact real-time sign language interpretation, interactive learning tools, and inclusive communication systems. Expanding the parallel corpus to include conversational and domain-specific data would significantly enhance the system's adaptability and practical use. The proposed approach to creating a parallel corpus for KSL offers a valuable framework that can be adapted in other countries, particularly within the post-Soviet region. This initiative brings several benefits, including language preservation, improved accessibility, and the promotion of intercultural communication. One crucial aspect of this approach is its role in preserving endangered languages. Many regional sign languages face the risk of extinction, and the combination of corpus development and deep learning can serve as a method for documenting and revitalizing these languages, ensuring their survival for future generations. Additionally, the creation of a parallel corpus facilitates intercultural communication both nationally and internationally. This is especially relevant in regions where sign languages share similarities or historical connections. The KSL project can serve as a model for collaborative efforts between countries to improve sign language research and accessibility. A detailed analysis of KSL's grammatical structure also represents a significant contribution to sign language linguistics. Researchers working with other sign languages can adapt this methodology to their specific linguistic contexts, enabling more comparative studies and advancing the broader field of sign language research. Technological advancements are another key aspect of this study. The use of deep learning, particularly the Transformer architecture, highlights the potential of advanced technologies in addressing challenges related to sign language processing. These innovations can be further adapted by other countries to suit their unique linguistic needs. The methodologies developed for KSL can also be extended to other languages, particularly within the Commonwealth of Independent States (CIS) and former Soviet republics, where related sign languages exist or where research on sign languages remains underdeveloped. Collaborative projects and knowledge sharing among researchers from different countries could accelerate progress in this area. This study addresses the resource gap for KSL, demonstrating the potential of NLP and machine learning in reducing communication barriers. Its findings contribute to advancements in sign language recognition while emphasizing the importance of inclusive, context-aware technological solutions. Ultimately, this research lays the groundwork for more accessible and inclusive systems, fostering greater communication opportunities for individuals with hearing and speech impairments.