# Creating a Parallel Corpus for the Kazakh Sign Language and Learning
## 5 Discussion  
This research provides valuable insights into the development of a machine translation system specifically designed for KSL, a linguistic field that has often been overlooked due to the limited availability of resources. By constructing a parallel corpus and analyzing the structural features of KSL, this study contributes to a deeper understanding of the complexities of sign language processing. It addresses both theoretical linguistic challenges and practical applications, particularly in assistive technologies aimed at supporting individuals with hearing and speech impairments in Kazakhstan. A significant achievement of this study is the identification of core word order patterns in KSL. More than 500 natural language sentences were analyzed and categorized into seven groups: reversible, nonreversible, locative, animate, inanimate, heavy, and nonheavy, the research highlights key differences between KSL grammar and spoken Kazakh. These differences go beyond theoretical analysis, emphasizing the need to adapt existing natural language processing (NLP) frameworks to accommodate the structural demands of sign language. Unlike spoken languages that follow a linear syntax, sign languages like KSL operate within a visual-spatial modality, requiring more specialized and sophisticated modeling approaches. A major technical breakthrough of this research is the development of a parser capable of converting Kazakh text into KSL glosses. This tool plays a crucial role in the creation of a comprehensive parallel corpus, which is essential for training machine learning models. This study further demonstrates the scalability and effectiveness of the system by applying Transformer-based machine translation models trained on diverse texts. The choice of Transformer models, known for their ability to capture complex contextual dependencies, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Type** & **Strengths** & **Weaknesses** & **Performance on Long Sentences** \\ \hline CNN [9] & Effective at detecting local patterns & Challenges in understanding & \\  & & global context & Low \\ \hline LSTM [10] & Good at sequential data, captures & & \\  & short-term dependencies & & \\ \hline
**Transformer** & Handles long sentences efficiently & & \\  & using attention mechanism & High computational cost & High \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of offer processing models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Approaches** & **Metric (BLEU Score)** & **Limitations** \\ \hline Syntax-Aware Transformer-Based & Incorporating Syntactic & & The scarcity of SL data and potential \\ Machine Translation for & Information into Machine & 53.52\% & performance degradation. \\ Text-to-Sign-Language Gloss [28] & Translation Models & & \\ \hline Progressive Transformers for & & & High computational costs, potential \\ End-to-End Sign Language & Progressive Transformers for & & overfitting to specific datasets, challenges \\ Production [PERSON] [29] & End-to-End & & in generalizing to different sign languages \\  & & & and dialects, and difficulties in aligning \\  & & & gestures with variations in text input. \\ \hline
**Our** & & **Transformer and Seq2 seq** & **27.50\%** & **Lack of uniform grammatical structures** \\  & & parallel corpus & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative table of methods and approaches. aligns this research with recent advancements in NLP, leading to promising translation accuracy results as indicated by BLEU scores. Despite these achievements, this study acknowledges several challenges. One of the most significant is the use of glosses as an intermediary step in the translation process. While glosses provide a practical method for building a parallel corpus, they often simplify the intricate visual-spatial grammar of sign language. To capture the full linguistic richness of KSL, future research could explore integrating video-based data and advanced multi-modal approaches.