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KyRuBench-20K: A Domain-Balanced Benchmark for Kyrgyz→Russian Machine Translation

Overview

KyRuBench-20K is a curated evaluation benchmark for Kyrgyz (кыргызча) to Russian (русский) machine translation. It contains 20,000 parallel sentence pairs drawn from three distinct domains with equal representation, enabling fair cross-domain evaluation of translation systems.

This benchmark addresses the lack of standardized, balanced evaluation resources for Kyrgyz — a low-resource Turkic language with ~5 million speakers.

Dataset Summary

Property	Value
Language pair	Kyrgyz (ky) → Russian (ru)
Total examples	20,000
Domains	3 (equally balanced)
Script	Cyrillic (both languages)
Split	Test only (evaluation benchmark)

Domain Distribution

Domain	Examples	Description
General Language	6,667	Parallel text from news, government, and general web sources
Mighty Kyrgyz (Yudahin)	6,666	Yudahin dictionary-based parallel corpus
Literature	6,667	Literary text (novels, short stories, poetry)

Quality Filtering

The benchmark was constructed through a rigorous multi-stage filtering pipeline:

Deduplication — removed duplicate source and target sentences
Length filtering — retained sentences with 20–300 characters on both sides
Length ratio — source/target character ratio between 0.5 and 2.0
Script validation — Cyrillic ratio ≥ 0.6 on both sides
Domain balancing — equal random sampling from each domain (seed=42)

After filtering, 20,000 sentences were sampled with equal domain representation.

Baseline Results

We evaluate 7 systems across three categories: dedicated MT, frontier LLMs, and web translators. All LLM-based systems use identical translation prompts for fair comparison.

Rank	System	Type	BLEU	chrF++	General	Yudahin	Literature
1	AirUn Translator + LLM	Dedicated MT	53.32	71.81	45.92	64.61	52.27
2	AirUn Translator	Dedicated MT	52.37	71.29	43.18	64.67	52.95
3	Claude Opus 4.6	Frontier LLM	45.49	67.83	48.99	49.64	36.66
4	GPT-5.4	Frontier LLM	43.53	66.59	47.58	48.60	33.68
5	Aitil Translate	Web Translator	42.15	64.35	46.91	47.27	31.33
6	Claude Sonnet 4.6	Frontier LLM	38.85	63.75	41.56	44.97	30.08
7	Grok 4.20	Frontier LLM	37.39	62.53	40.63	43.39	27.92

Key Findings

Dedicated MT systems significantly outperform frontier LLMs on Kyrgyz→Russian, with a gap of 8–16 BLEU points
Literature domain exposes the largest quality gap between dedicated MT (BLEU ~52) and frontier LLMs (BLEU ~28–37)
General Language domain is the most competitive, with some LLMs approaching dedicated MT performance
Claude Opus 4.6 is the strongest LLM for Kyrgyz, followed by GPT-5.4
Kyrgyz remains a challenging low-resource language even for state-of-the-art LLMs

Usage

from datasets import load_dataset

dataset = load_dataset("BDigit/KyRuBench-20K")

# Access the test split
for example in dataset["test"]:
    print(example["source"])   # Kyrgyz sentence
    print(example["target"])   # Russian reference translation

Evaluation

We recommend evaluating with sacreBLEU for reproducibility:

import sacrebleu

# After generating hypotheses
bleu = sacrebleu.corpus_bleu(hypotheses, [references])
chrf = sacrebleu.corpus_chrf(hypotheses, [references], word_order=2)  # chrF++

print(f"BLEU: {bleu.score:.2f}")
print(f"chrF++: {chrf.score:.2f}")

Important: Always report per-domain scores alongside overall metrics. The three domains test fundamentally different translation challenges.

Data Fields

source (str): Sentence in Kyrgyz (Cyrillic script)
target (str): Reference translation in Russian (Cyrillic script)

Intended Use

Benchmarking machine translation systems for Kyrgyz→Russian
Evaluating multilingual LLMs on low-resource Turkic language translation
Comparing dedicated MT systems against general-purpose language models
Studying domain effects on translation quality for low-resource languages

Limitations

Single reference: Each source has one reference translation. Multi-reference evaluation would give more robust scores.
Direction: Currently Kyrgyz→Russian only. Russian→Kyrgyz evaluation requires separate references.
Literature domain: Literary text availability is limited, constraining future expansion of this domain.
Reference quality: References may contain occasional noise from the original parallel corpus.

Citation

@misc{kyrubench2026,
  title={KyRuBench-20K: A Domain-Balanced Benchmark for Kyrgyz-Russian Machine Translation},
  author={Alibekov, Nurtilek and Kumarbai uulu, Bektemir and Uvalieva, Zarina and Tashbaltaev, Tynchtykbek and Metinov, Adilet},
  year={2026},
  organization={Bdigital LLC},
  howpublished={\\url{https://huggingface.co/datasets/BDigit/KyRuBench-20K}},
}

License

CC-BY-NC-ND-4.0 (Attribution, Non-Commercial, No Derivatives)

For commercial licensing inquiries, contact an@bdigital.kg.

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