# Creating a Parallel Corpus for the Kazakh Sign Language and Learning
## 1 Introduction
The key challenges faced by researchers in the field of KSL and parallel corpus development include the following:  
* _Preservation and Dissemination of Cultural Heritage:_ KSL is not only a means of communication but also an essential part of the cultural heritage of individuals with hearing and speech impairments in Kazakhstan. The development of a parallel corpus plays a crucial role in preserving and promoting this cultural asset, ensuring its continued use and recognition. * _Promoting Access to Education:_ Individuals with hearing and speech impairments often struggle to access education due to the limited distribution of KSL. A parallel corpus can support the development of KSL teaching materials and methodologies, making education more accessible. * _Facilitating Communication and Collaboration:_ The creation and training of a KSL corpus enable the development of tools that improve communication between deaf and hearing individuals. These tools can facilitate translation from KSL into text or spoken language, thereby enhancing accessibility. _Advancing Linguistic Research:_ A parallel corpus serves as a valuable resource for studying KSL's grammatical structures, vocabulary, and linguistic organization, contributing to advancements in sign language linguistics. * _Promoting Social Integration and Inclusion:_ Strengthening KSL and developing a well-structured corpus contribute to the social integration of individuals with hearing and speech impairments, allowing them to participate more actively in society. * _Supporting Professional Interpreters:_ A parallel corpus provides an essential resource for interpreters and translators working with deaf and hard-of-hearing individuals, enabling more accurate and effective communication. These challenges and goals drive researchers in the field of KSL and parallel corpus development, fostering efforts to enhance communication, integration, and quality of life for people with hearing and speech impairments in Kazakhstan and beyond. A key achievement of this project is the creation of a parallel corpus of Kazakhstan-language texts and KSL glosses, which greatly advances linguistic research and supports language acquisition for KSL users. This study addresses the limited availability of annotated sign language data, enabling statistical analysis and the development of machine learning models that improve in accuracy as data volume increases. This initiative is particularly groundbreaking in Kazakhstan, where such resources have previously been unavailable. The development of a custom parser to automate the transformation of Kazakhstan-language text into KSL glosses represents a significant technical innovation. This parser accelerates data generation for machine translation models while mitigating the limitations of manual annotation. Using a rule-based approach informed by an analysis of KSL word order and grammar, it effectively processes unique syntactic structures, ensuring precise alignment between Kazakh text and KSL glosses. This research presents an in-depth linguistic analysis of KSL grammar, focusing on key features such as word order, animacy, reversibility, and locative cases. This study highlights significant differences between spoken Kazakh and KSL, providing valuable insights into KSL's grammatical structure. These findings directly inform the design of the parser and the architecture of machine translation models, ensuring alignment with KSL's linguistic properties. The application of Transformer-based deep learning models for Kazakh-to-KSL gloss translation represents a novel contribution. This study demonstrates the effectiveness of Transformers in capturing complex dependencies within KSL, achieving high translation accuracy as validated by BLEU scores. Their ability to process long sequences and model spatial-temporal relationships makes them particularly well-suited for the intricacies of sign language translation. The dataset and methodologies developed in this study contribute to significant advancements in gesture recognition.