Recent Advancements and Challenges of T urkic C entral A sian Language Processing (2025)

Abstract: Research in NLP for Central Asian Turkic languages - Kazakh, Uzbek, Kyrgyz, and Turkmen - faces typical low-resource language challenges like data scarcity, limited linguistic resources and technology development. However, recent advancements have included the collection of language-specific datasets and the development of models for downstream tasks. Thus, this paper aims to summarize recent progress and identify future research directions. It provides a high-level overview of each language’s linguistic features, the current technology landscape, the application of transfer learning from higher-resource languages, and the availability of labeled and unlabeled data. By outlining the current state, we hope to inspire and facilitate future research.

## introduction turkic languages are spoken by approximately 200 million people worldwide, with a significant concentration in central asia (see detailed breakdown of the number of speakers in figure 1 ). while turkish is the most resourceful language in the family, this paper focuses on less-resourced turkic languages that are geographically, historically, and linguistically closer to one another in central asia. these languages represent an important subset of the turkic family, spoken by approximately 80 million people in the region. like any other low-resource language speakers, speakers of central asian languages would benefit from having reliable language technology, from simple spell checkers to virtual assistants. such tools would uphold newly adopted language policies and cement the role of local languages in the region. developing these resources, however, requires the existence of open-source datasets and up-to-date language models. to address these resource limitations, researchers are exploring methods like transfer learning and data augmentation, though both have limitations in task applicability and effectiveness @xcite . this paper aims to provide an overview of existing resources and suggest directions for future research to support both those utilizing current resources and those developing new ones (for the search strategy details see appendix a). we also seek to highlight current resource needs, addressing which of those could be particularly crucial in advancing the turkic central asian languages toward a higherresource status. ## related work recently, substantial efforts have been made to consolidate domain knowledge for turkic languages via linguistic analysis tools @xcite , and nlp technology assessments @xcite . however, there is still a lack of comprehensive research summarizing the available data and language processing tools, especially for central asian turkic languages. while state-of-the-art advancements in speech recognition and machine translation exist for some languages @xcite , no cross-linguistic comparisons have been conducted. a detailed survey could provide a valuable foundation for comparison and help define future research directions. ## datasets availability open-source language resources enable researchers to scale and reuse data, reducing overall time spent on corpora collection and evaluation. however, data access varies from low to almost non-existent for the languages considered in the paper. a full summary of all the reviewed datasets and their primary features can be found in the appendix b. ## reasons of data scarcity data scarcity in central asian languages stems from the widespread use of russian, limited internet access, and a lack of ai-focused educational and technological initiatives. ## application of transfer learning the situation when one of the languages in a language family is more resourceful than others is not unique to turkic languages. this opens the door for potential transfer learning from turkish or one of the central asian languages to the other languages within the same family. usually, this is more attainable than collecting large datasets from scratch. transfer learning has been substantially studied in the domain of machine translation, and the choice of parent language has been highlighted as an important criteria for the technique application @xcite . combining transliteration and bytepair encoding, nguyen and chiang (2017) proved that this transfer learning approach might be suitable beyond high-resource to low-resource pairs, extending to pairs within low-resource only, especially the ones belonging to the agglutinative languages. the greater availability of nlp tools for kazakh made it possible to assess transfer learning potential for some turkic laguages lying beyond the scope of this paper, namely, tatar @xcite . other studies on transfer learning from kazakh have been conducted on the task of asr @xcite . some of them also aimed at using russian as a source language, but the efforts proved to be less successful @xcite . ## data augmentation, transliteration, and r-drop regularization apart from transfer learning, data augmentation techniques, r-drop regularization, and transliterations have been substantially researched for enhancing model performance in some turkic languages. a study on the topic of sentence augmentation using large language models for kazakh @xcite demonstrated that data augmentation works well for already resourceful languages and does so less successfully in the lowresource domain, which might seem somewhat obvious. however, practical applications using other data augmentation techniques, including phrase replacement, proved to significantly improve the bleu score of certain language pairs, for example, kazakh-chinese @xcite . data augmentation with r-drop regularization has also proved useful for the same kazakh-chinese translation task in other studies @xcite . other techniques, such as dropout and transliteration, are often applied alongside transfer learning and data augmentation. furthermore, multilingual models may outperform those using only transfer learning on certain tasks @xcite . altogether, these various techniques allow researchers to experiment with central asian language processing without requiring extensive data collection. ## conclusion significant improvements in the processing of turkic central asian languages have been achieved in the recent years. however, there still exists an imbalance in the amount of available data and technology, with kazakh and uzbek dominating the scene, and kyrgyz and turkmen requiring significant efforts in terms of data collection. besides leveraging already existing datasets or web-sources for curating new data using data augmentation, other potentially successful options for technological advancements include transfer learning from kazakh to uzbek, kyrgyz, and turkmen. already existing data in kazakh might prove useful for the studies of other central asian languages, given their close linguistic relatedness. while significant efforts are yet to emerge and be maintained for kazakh, uzbek, kyrgyz, and turkmen to reach the level of winners or underdogs @xcite , a substantial work has already been developed in the most crucial areas of natural language processing, including the automatic speech recognition systems and machine translation. with the potential of transferring this experience to other tasks, such as information retrieval, text generation, and question answering, there is a greater hope for progress towards the state-of-theart technology for these central asian languages.