# Creating a Parallel Corpus for the Kazakh Sign Language and Learning
## 4 Results  
A study on the basic structure of KSL was conducted, providing insights into word order and the general structure of sign language. More than 10,000 sign language sentences were analyzed and classified into categories such as local case, animacy, reversibility, time, direction, and others. The process of creating a parallel corpus consists of three key stages, each essential for optimizing translation and adaptation between Kazakh and KSL (see Figure 4). Stage A: This stage involves the translation of text into gloss, where corresponding phrases in Kazakh and KSL are aligned. This step serves as the foundation for further analysis and text alignment. Stage B: Sentence length distribution in Kazakh and KSL is analyzed and visualized through graphs. The comparison reveals that both languages generally use short sentences; however, Kazakh exhibits a wider range of sentence lengths, whereas KSL follows a more compact distribution. As a result, longer sentences in Kazakh may need to be divided into shorter segments when translated into KSL. Stage C: The characteristics of the Kazakh and KSL corpora are compared, including key metrics such as the number of sentences, number of tokens, maximum and minimum sentence lengths, and vocabulary size. These data provide valuable insights into the structural and complexity differences between the two languages, aiding in the accurate alignment and translation of parallel texts. Together, these three stages form a structured approach to improving translation between Kazakh and KSL. During this study, it became evident that the grammar of sign language differs significantly from that of spoken language, influencing the transformation rules for each of the designated categories. Based on these findings, a set of rules was developed, and a parser was created to convert Kazakh text into sign language text. Once the parser was completed, the model was trained on a dataset of over 100,000 sentences, sourced primarily from books and news articles. The training process was carried out using Transformer architecture. In this study, the results of parallel corpus training were compared to assess the performance of the two most widely used machine translation system architectures (see Figure 5). To assess the translation efficiency, the BLEU criterion was used. This algorithm evaluates the quality of machine-generated translations by comparing them to human translations. The quality assessment is based on the degree of similarity between the machine-generated text and the reference translation produced by a professional translator: \"The more the machine translation matches the professional human translation, the higher its quality\".--this is the key idea of BLEU. Figure 6 presents the BLEU scores for the Seq2 seq and Transformer models. The Seq2 seq model starts with an initial BLEU score of approximately 0.625, gradually improving and reaching a peak of around 0.675. In contrast, the Transformer model begins with a higher BLEU score of approximately 0.72. Consistent growth is observed throughout all training epochs, with the score reaching a maximum of approximately 0.775 in the fifth epoch. At every stage of training, the Transformer model outperforms the Seq2 seq model, demonstrating superior translation accuracy. A comparison of the various methods employed in the field of sign language translation reveals a spectrum of performance and limitations. The syntax-aware transformer-based approach for text-to-sign-language gloss achieves a BLEU score of 53.52%, a result that is enhanced by the incorporation of syntactic information but constrained by the scarcity of sign language data and the potential for performance degradation.