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KyrgyzNLP: Challenges, Progress, and Future

Anton Alekseev, Timur Turatali

TL;DR

The current state of the NLP field in the specific Kyrgyz tili language, noting that many of the publicly available resources have only recently been developed, with few exceptions beyond dictionaries, and proposing a roadmap for future development in terms of research topics and language resources.

Abstract

Large language models (LLMs) have excelled in numerous benchmarks, advancing AI applications in both linguistic and non-linguistic tasks. However, this has primarily benefited well-resourced languages, leaving less-resourced ones (LRLs) at a disadvantage. In this paper, we highlight the current state of the NLP field in the specific LRL: kyrgyz tili. Human evaluation, including annotated datasets created by native speakers, remains an irreplaceable component of reliable NLP performance, especially for LRLs where automatic evaluations can fall short. In recent assessments of the resources for Turkic languages, Kyrgyz is labeled with the status 'Scraping By', a severely under-resourced language spoken by millions. This is concerning given the growing importance of the language, not only in Kyrgyzstan but also among diaspora communities where it holds no official status. We review prior efforts in the field, noting that many of the publicly available resources have only recently been developed, with few exceptions beyond dictionaries (the processed data used for the analysis is presented at https://kyrgyznlp.github.io/). While recent papers have made some headway, much more remains to be done. Despite interest and support from both business and government sectors in the Kyrgyz Republic, the situation for Kyrgyz language resources remains challenging. We stress the importance of community-driven efforts to build these resources, ensuring the future advancement sustainability. We then share our view of the most pressing challenges in Kyrgyz NLP. Finally, we propose a roadmap for future development in terms of research topics and language resources.

KyrgyzNLP: Challenges, Progress, and Future

TL;DR

The current state of the NLP field in the specific Kyrgyz tili language, noting that many of the publicly available resources have only recently been developed, with few exceptions beyond dictionaries, and proposing a roadmap for future development in terms of research topics and language resources.

Abstract

Large language models (LLMs) have excelled in numerous benchmarks, advancing AI applications in both linguistic and non-linguistic tasks. However, this has primarily benefited well-resourced languages, leaving less-resourced ones (LRLs) at a disadvantage. In this paper, we highlight the current state of the NLP field in the specific LRL: kyrgyz tili. Human evaluation, including annotated datasets created by native speakers, remains an irreplaceable component of reliable NLP performance, especially for LRLs where automatic evaluations can fall short. In recent assessments of the resources for Turkic languages, Kyrgyz is labeled with the status 'Scraping By', a severely under-resourced language spoken by millions. This is concerning given the growing importance of the language, not only in Kyrgyzstan but also among diaspora communities where it holds no official status. We review prior efforts in the field, noting that many of the publicly available resources have only recently been developed, with few exceptions beyond dictionaries (the processed data used for the analysis is presented at https://kyrgyznlp.github.io/). While recent papers have made some headway, much more remains to be done. Despite interest and support from both business and government sectors in the Kyrgyz Republic, the situation for Kyrgyz language resources remains challenging. We stress the importance of community-driven efforts to build these resources, ensuring the future advancement sustainability. We then share our view of the most pressing challenges in Kyrgyz NLP. Finally, we propose a roadmap for future development in terms of research topics and language resources.

Paper Structure

This paper contains 78 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. On the Importance of Data in NLP
  3. Motivating Examples
  4. Processing Methods for Less-Resourced Languages
  5. LLMs as a “Universal Solution”?
  6. Safety, Decision-Making, and Model Evaluation.
  7. Cultural Preservation.
  8. Innovation and Research.
  9. Human Oversight.
  10. Model Limitations.
  11. Kyrgyz as a Less-Resourced Language
  12. The Kyrgyz Language: An Overview
  13. Linguistic and Demographic Profile.
  14. Certain Linguistic Characteristics.
  15. Linguistic Relations
  16. ...and 63 more sections

Figures (4)

  • Figure 1: Distribution of the total number of labels (note: some articles may have multiple labels, so the total does not equal the number of articles).
  • Figure 2: Collaboration (co-authorship) network based on the Kyrgyz NLP bibliography. Nodes represent individual authors, and an edge between two nodes indicates co-authorship on at least one work.
  • Figure 3: Milestones.
  • Figure 4: Timelines with scenarios.