The K yrgyz Seed Dataset Submission to the WMT 25 Open Language Data Initiative Shared Task (2025)

Abstract: We present a Kyrgyz language seed dataset as part of our contribution to the WMT25 Open Language Data Initiative (OLDI) shared task. This paper details the process of collecting and curating English–Kyrgyz translations, highlighting the main challenges encountered in translating into a morphologically rich, low-resource language. We demonstrate the quality of the dataset through fine-tuning experiments, showing consistent improvements in machine translation performance across multiple models. Comparisons with bilingual and MNMT Kyrgyz-English baselines reveal that, for some models, our dataset enables performance surpassing pretrained baselines in both English–Kyrgyz and Kyrgyz–English translation directions. These results validate the dataset’s utility and suggest that it can serve as a valuable resource for the Kyrgyz MT community and other related low-resource languages.

## introduction while machine translation has advanced significantly, progress remains uneven, with morphologically rich, low-resource languages facing substantial obstacles @xcite . this disparity is particularly acute for the turkic language family, where agglutinative structures pose unique challenges to standard mt architectures. kyrgyz (кыргыз тили), a turkic language with approximately 5.5 million speakers, exemplifies this situation. despite a growing number of large, automatically-mined parallel datasets @xcite , there is a significant shortage of high-quality, human-curated resources essential for building robust and reliable translation systems. this paper details our contribution to the open language data initiative (oldi) shared task: the creation of a high-quality, human-validated english-kyrgyz dataset of 6,193 sentence pairs. the source text, drawn from diverse scientific domains on wikipedia, provides rich terminological and syntactic complexity that inherently challenges mt systems. for a low-resource language like kyrgyz, this complexity exposed a more fundamental obstacle: the absence of standardized scientific vocabulary. this makes high-quality human translation not just a matter of review, but of active linguistic curation. our translation process, therefore, required developing novel strategies to handle significant terminological gaps and neologisms. this involved our team of native speakers making crucial terminological choices. for instance, we prioritized the native kyrgyz word "дене" for scientific compounds like "celestial body" ("космостук дене") and "antibody" ("антидене") over the somewhat established russian calque "тело", a decision that reflects modern usage and enhances both accuracy and naturalness. our main contributions are as follows: our results confirm that even a modest amount of high-quality parallel data is critical for advancing mt performance for structurally divergent language pairs, particularly when involving a low-resource language. foot_0 2 related work despite kyrgyz being classified as a lowresource language, recent years have witnessed notable progress in the development of machine translation (mt) systems for this language @xcite . the very first machine translation systems for kyrgyz were rule-based, representing foundational efforts in the field. a key contribution in this area is the open-source finite-state morphological transducer for kyrgyz developed by @xcite . this transducer, developed within the apertium platform, was a critical component for a prototype turkish-kyrgyz machine translation system and laid the groundwork for further language pairs, including an in-progress kazakh-kyrgyz system. alkım and çebi (2019 later, the authors released a multi-way multilingual model neural mt model (mnmt) for these languages, showing that the multilingual model outperforms almost all bilingual baselines @xcite . fine-tuning multilingual models for lowresource languages has shown considerable promise @xcite . notably, kyrgyz has been incorporated into several multilingual nmt models, such as mt5 @xcite , leading to improved translation quality. a particularly significant contribution to the field is the multilingual nllb-200 model, trained on 200 languages including @xcite . the primary objective of this work was to provide extensive coverage of low-resource languages within a unified framework. another line of research aimed at improving machine translation quality focuses on enhancing tokenization methods. for example, the study by @xcite proposes a tokenization approach based on the complete set of endings (cse), which reduces vocabulary size and, as demonstrated on kazakh-english translation tasks, yields better generation quality compared to byte pair encoding (bpe) segmentation. similarly, @xcite investigate the kyrgyz-kazakh language pair and develop a morphological tokenizers based on the relational segmentation model. the scientific contribution of this article is the creation of the morphological tokenizer for kyrgyz and kazakh, as well as fine-tuning experiments with this dataset of the neural model t5-small. ## kyrgyz-english parallel datasets another important line of work in improving machine translation involves the creation of multilingual parallel datasets. below is a brief overview of open parallel datasets that include the kyrgyz language. as part of the turklang-7 project @xcite , a corpus of parallel sentences was compiled for russian and seven turkic languages. for kyrgyz, the collection includes 426,190 parallel sentence pairs; however, the final version of the dataset has not been made publicly available. the nllb v1 dataset foot_1 is a large english-kyrgyz parallel corpus containing 21,360,637 sentence pairs, produced during the development of the nllb-200 model @xcite , where kyrgyz was included among the languages for which bilingual translation pairs were automatically collected. however, the quality of this dataset is limited, as demonstrated by our manual analysis of the public opus sample, which comprises 49 examples 3 . the fully annotated sample that forms the basis for this analysis, including examples of misalignment and various error types, is provided in appendix a. our analysis revealed that only 59.18% of the sentences labeled as kyrgyz were actually written in kyrgyz. furthermore, of this reduced kyrgyz subset, a mere 55.17% were deemed to be accurate and fluent translations. this indicates that only about 32.65% of the original sample represents a high-quality, usable translation pair. kyrgyz language was also incorporated into a set of 14 low-resource languages for which the gourmet project team compiled parallel datasets @xcite . in total, 14,498 kyrgyz-english parallel sentences and 23,017 kyrgyz-russian parallel sentences were assembled. these sentences were obtained through machine translation followed by editorial validation and control. the open language data initiative (oldi) is a collaborative project that enables language communities, researchers, and developers to contribute to foundational datasets essential for machine learning and natural language processing. at present, oldi foot_3 maintains two key datasets: oldi-seed and flores+. the oldi-seed dataset contains 6,193 english sentences paired with translations into approximately 40 low-resource languages. the english source sentences were drawn from a diverse range of wikipedia articles covering fields such as biology, astronomy, the arts, history, mathematics, etc. we use the english corpus as a source dataset for kyrgyz translations. flores+ is an evaluation benchmark consisting of two subsets -a test set of 1012 sentences and a validation set of 997 sentences -each professionally translated into 200 languages by expert linguists. the x-wmt benchmark @xcite is a test set designed to evaluate machine translation quality for turkic languages. it is based on the professionally translated english-russian corpus from the wmt 2020 news translation task. the original news sentences were subsequently translated into eight turkic languages. for the english-kyrgyz language pair, the benchmark comprises 500 sentences. ## language description kyrgyz (also known as kirgiz or kirghiz) is a turkic language of the kipchak and/or the south siberian branch (iso 639-2: kir, glottocode: kirg1245). it is spoken primarily in kyrgyzstan, where it holds official status due to it being the national language. however, it does spread further to central asian countries, specifically in gorno-badakhshan autonomous region of tajikistan and it is also considered to be a minority language in the kizilsu kyrgyz autonomous prefecture in xinjiang, china. and another regional variant of the kyrgyz language, referred to as pamiri kyrgyz, is spoken in northeastern afghanistan and northern pakistan. from a phonetic and phonological standpoint, the turkic languages most closely resembling kyrgyz are the southern dialects of altay, although kyrgyz also shares significant similarities with kazakh @xcite . additionally, the southern varieties of kyrgyz exhibit distinctive features that align with uzbek, which are not present in any other kyrgyz dialects. approximately, 5.5 million people primarily in kyrgyzstan consider kyrgyz their native language. ## conclusion this study, part of the oldi initiative, contributed 6,193 english-kyrgyz sentence pairs and showed that even modest, carefully curated data can improve neural machine translation for lowresource, morphologically complex languages. in this work, we addressed three key challenges: adapting to agglutinative morphology, restructuring complex subordination, and maintaining terminological consistency. our preference for native kyrgyz terms over russian calques reflects contemporary usage and contributes to natural language evolution. fine-tuning mbart, m2m100, mt5, and nllb-200 models on our dataset yielded consis-tent performance gains, validating the value of linguistically-informed data. however, the manual evaluation of model translations, highlight the need for further research to achieve productionquality nmt for kyrgyz. our collaborative workflow-combining mt, manual correction, and consistency review-offers a replicable methodology for other low-resource languages. future work involves expanding the dataset and integrating community feedback. this research provides concrete resources and a methodological framework for community-driven language technology development, balancing technical advancement with cultural-linguistic authenticity.