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This arXiv paper proposes routing Mongolian translation through the better-resourced Cyrillic script before translating, and reports BLEU and COMET gains over direct translation across several LLM backbones. Its headline claim that open-source models can match GPT-4.1 is specific to the authors' evaluation setup, so readers should check the benchmarks and baselines before generalizing. The more transferable idea is using a script or resource hierarchy as a pivot, plus generating synthetic parallel data from scarce script variants, which may apply to other digraphic or low-resource languages.
CoPiT: Cognitive Pivot Translation for Digraphic Low-Resource Mongolian in the Traditional Script
arXiv:2607.05849v2 Announce Type: replace Abstract: Low-resource languages remain challenging for machine translation, and Mongolian is a representative case. As a digraphic language, Mongolian is written in both Cyrillic and Traditional scripts, which exhibit a severe imbalance in data availability. While the Cyrillic script is relatively well-resourced, the Traditional script remains extremely data-scarce and orthographically ambiguous, leading to substantial performance degradation in direct translation. We propose CoPiT, a cognitively motivated pivot-based translation pipeline that exploits this internal resource hierarchy by routing translation through the Cyrillic script. The pipeline explicitly resolves script-induced ambiguity in the Traditional script before translation, enabling more stable and accurate meaning transfer. Across multiple backbone models and target languages, CoPiT generally improves over direct translation, with substantial absolute BLEU improvements and up to 1.5-1.6x gains in COMET. These gains allow strong open-source models to match or outperform GPT-4.1 under comparable evaluation settings. Beyond inference-time improvements, CoPiT enables the construction of synthetic parallel data directly from Traditional-script text, mitigating data scarcity in realistic low-resource scenarios. We release a new multi-script parallel dataset covering Mongolian in both scripts alongside English, Korean, and Russian.
Abstract:Low-resource languages remain challenging for machine translation, and Mongolian is a representative case. As a digraphic language, Mongolian is written in both Cyrillic and Traditional scripts, which exhibit a severe imbalance in data availability. While the Cyrillic script is relatively well-resourced, the Traditional script remains extremely data-scarce and orthographically ambiguous, leading to substantial performance degradation in direct translation. We propose CoPiT, a cognitively motivated pivot-based translation pipeline that exploits this internal resource hierarchy by routing translation through the Cyrillic script. The pipeline explicitly resolves script-induced ambiguity in the Traditional script before translation, enabling more stable and accurate meaning transfer. Across multiple backbone models and target languages, CoPiT generally improves over direct translation, with substantial absolute BLEU improvements and up to 1.5-1.6x gains in COMET. These gains allow strong open-source models to match or outperform GPT-4.1 under comparable evaluation settings. Beyond inference-time improvements, CoPiT enables the construction of synthetic parallel data directly from Traditional-script text, mitigating data scarcity in realistic low-resource scenarios. We release a new multi-script parallel dataset covering Mongolian in both scripts alongside English, Korean, and Russian.
Computation and Language (cs.CL)
arXiv:2607.05849 [cs.CL]
(or arXiv:2607.05849v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.05849 Focus to learn more arXiv-issued DOI via DataCite
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