Recent Advancements and Challenges of Turkic Central Asian Language Processing

TL;DR

This paper reviews recent progress in NLP for Turkic Central Asian languages, highlighting advancements in datasets, models, and transfer learning, while discussing ongoing challenges and future research directions.

Contribution

It provides a comprehensive overview of recent developments and identifies key challenges and opportunities for NLP in low-resource Turkic languages.

Findings

Development of language-specific datasets

Application of transfer learning techniques

Identification of resource scarcity challenges

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.