# Creating a Parallel Corpus for the Kazakh Sign Language and Learning
## 2 Related Works  
A parallel corpus plays a crucial role in the development of avatar-based sign language translation systems. As part of this study [11], a comprehensive review of the existing research was conducted. One study focused on American Sign Language (ASL) and introduced a new algorithm for converting English sentences into ASL. The researchers developed a parser that automatically translates sentences and includes an interface for user interaction. The study utilized a dataset of 880 words, including English glosses and ASL, combined with transformation rules to construct a bilingual text corpus containing 800 million words. However, it is important to note that this corpus did not account for semantic aspects or verb types in sign language, such as \"agreement\" and \"disagreement\" verbs. In a related study [12], a German public television channel, PHOENIX, broadcast news and weather forecasts with sign language interpretation daily over a three-year period (2009-2011). Transcription was conducted for weather forecasts from 386 news broadcasts, performed by native deaf and hard-of-hearing speakers of German Sign Language (DGS). Additionally, the German weather forecasts were transcribed semi-automatically using the RASR speech recognition system. To enhance translation possibilities, an additional version of the glossaries was translated into spoken German. In 2018, the DGS-Korpus project team released the first publicly accessible version of the DGS corpus, marking a significant milestone in its development [13]. Initially, the focus was on expanding the corpus, but following its release, efforts shifted toward content enrichment. New data formats were introduced, annotation agreements were established, and OpenPose data for all transcripts were published. The research portal and community websites continued to evolve, incorporating persistent identifiers, archival versions of past releases, and mobile-friendly enhancements. The portal itself underwent substantial upgrades, including an improved web transcript viewer, a KWIC comparison tool, links to other DGS linguistic resources, and a German-language interface in addition to English. This article outlines the evolution of the DGS corpus, covering its first release in 2018, second release in 2019, and third release in 2020. Several key methods have been employed in the field of sign language translation research [14; 15; 16], each aiming to enhance translation performance and improve model efficiency across diverse datasets. (a) The first study introduced a key-point normalization method to improve the direct translation of sign language videos into spoken language. This approach uses stochastic frame selection and enlargement techniques to adapt to different body parts and enhance translation accuracy. (b) The second study proposed an encoder-decoder-based Transformer architecture, enabling continuous sign language recognition and translation in parallel. This model simplifies data collection and improves sign language recognition without gloss-level annotations, achieving a 30% efficiency increase. (c) The third study developed an alternative key-point normalization method for sign language translation without gloss annotations. The model was adapted to multiple databases, reducing information loss in videos through an improved frame selection technique, with its effectiveness confirmed through experimental evaluations. These methods collectively contribute to advancements in sign language recognition and translation by improving data processing efficiency and enhancing model performance, particularly when applied across diverse datasets. Additionally, research in lexicography and parallel corpus development [17,18] has explored the composition, structure, and effectiveness of sign language corpora. Two key studies were analyzed: the Anglo hryvian parallel Corps (EGPC) and the Swiss three Sign Language parallel Corps (SwissSLi). Both studies introduce new opportunities for lexicography and machine translation. However, each face unique challenges.