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Article · Herald of Kazakh-British technical university · September 26, 2026

BENCHMARKING DEEP LEARNING MODELS FOR FEW-SHOT CLASSIFICATION OF KAZAKH NEWS TEXTS UNDER LIMITED ANNOTATION

Duman Marlambekov, A. Ahmetova, Saule Torekul, Irina Ualiyeva

Saule Torekul
Open via DOIPDF

Abstract

Deep learning methods and transformer architectures have fundamentally reshaped the field of natural language processing; however, these advancements remain unevenly distributed. While high-resource languages like English benefit from large-scale benchmarks, robust text classification for low-resource languages, such as Kazakh, continues to pose a significant challenge. This paper presents a comparative analysis of the classical Word2Vec embedding, BiLSTM recurrent models, BERT and XLM-R transformers, the generative mT5 model, alongside classical and statistical baselines for few-shot Kazakh news text classification under limited annotation constraints. Experiments were conducted on the KazNews dataset, curated for few-shot classification from tengrinews.kz and inform.kz articles, as well as parallel HuffPost corpora. The datasets comprise five categories (sports, politics, business, travel, etc.). The experimental findings in 1-shot and 5-shot settings demonstrate that no single model consistently dominates across all datasets and regimes: Word2Vec achieved the best performance on the HuffPost corpora in the 5-shot regime (0.48 and 0.53); mT5-Seq2Seq outperformed others on KazNews under 5-shot (0.71); while BERT remained competitive on the long-text KazNews dataset in the 5-shot setup. The authors also observed a sharp performance gain for the mT5-Seq2Seq model in the 5-shot setup. Conversely, none of the evaluated models yielded acceptable quality metrics in the 1-shot regime.

Topic ModelingDomain Adaptation and Few-Shot LearningNatural Language Processing Techniquesfew-shot text classificationKazakh languagelow-resource languages

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