Kyrgyz Text Normalization: A Comparative Study of Neural and Rule-Based Approaches

2026-06-26 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, medium

Summary

A systematic study introduces the first automatic text normalization for Kyrgyz, a morphologically rich, low-resource Turkic language. Researchers compiled a 1.67 million noisy-clean text pair dataset from YouTube comments, Instagram posts, and Telegram channels, annotated by Gemini 3 Pro, with a 1,000-example test set human-verified. An additional 538 MB Kyrgyz corpus was used for continual pre-training. Five systems were evaluated: a rule-based baseline, zero-shot mT5, fine-tuned mT5-small, continually pre-trained mT5-small, and zero-shot Gemma 4. The fine-tuned mT5-small model achieved a Character Error Rate (CER) of 0.0796, significantly outperforming the rule-based baseline (CER 0.2029) and zero-shot Gemma 4 (CER 0.1620). Human evaluation confirmed 99.8% correctness for the fine-tuned mT5-small. The study also revealed that continual pre-training did not improve performance, with 87.5% of inspected failures due to hallucination.

Key takeaway

For NLP Engineers developing tools for low-resource languages like Kyrgyz, you should prioritize fine-tuning smaller models such as mT5-small over relying on larger, zero-shot models. Your efforts should focus on creating high-quality, human-verified datasets for fine-tuning, as this approach yielded 99.8% correctness. Be wary of continual pre-training, as it may introduce hallucinations and not improve performance for text normalization tasks.

Key insights

Fine-tuned mT5-small excels at Kyrgyz text normalization, outperforming larger zero-shot models and rule-based approaches.

Principles

• Fine-tuning small models can surpass larger zero-shot models.
• Continual pre-training may not always improve fine-tuning.
• Hallucination is a key failure mode in text normalization.

Method

Collected 1.67M noisy-clean text pairs from social media, annotated with Gemini 3 Pro, and human-verified a 1,000-example test set. Evaluated rule-based and neural models, including fine-tuned mT5-small.

In practice

• Prioritize fine-tuning smaller models for low-resource NLP.
• Verify LLM-generated training data with human experts.
• Analyze failure modes like hallucination in normalization tasks.

Topics

• Kyrgyz Language Processing
• Text Normalization
• Low-Resource NLP
• mT5-small
• Neural Language Models
• Dataset Annotation

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.