CoPiT: Cognitive Pivot Translation for Digraphic Low-Resource Mongolian in the Traditional Script

2025-12-26 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

CoPiT, a Cognitive Pivot Translation pipeline, addresses machine translation challenges for low-resource, digraphic languages like Mongolian, specifically its Traditional script. This script is data-scarce and orthographically ambiguous, causing poor direct translation performance. CoPiT routes translation through the relatively better-resourced Cyrillic script, explicitly resolving script-induced ambiguity before translation. It consistently outperforms direct translation across multiple backbone models (Qwen-3, Ministral-3, GPT-4.1) and target languages (English, Korean, Russian), achieving substantial absolute BLEU improvements and 1.5–1.6x COMET gains. Fine-tuned open-source CoPiT models can match or exceed GPT-4.1 performance. Beyond inference, CoPiT enables synthetic parallel data construction, mitigating data scarcity. A new multi-script parallel dataset covering Mongolian in both scripts, English, Korean, and Russian is publicly released.

Key takeaway

For NLP engineers developing machine translation systems for low-resource, digraphic languages, consider implementing a pivot-based approach like CoPiT. This method effectively resolves script-level ambiguities by routing through a more resourced intermediate script, significantly improving translation quality and enabling synthetic data generation. You can achieve performance comparable to or exceeding proprietary models like GPT-4.1, even with fine-tuned open-source backbones, by adopting this structured disambiguation strategy.

Key insights

CoPiT improves low-resource digraphic machine translation by pivoting through a better-resourced, less ambiguous script.

Principles

• Exploit internal resource hierarchies in digraphic languages.
• Decouple script ambiguity from semantic transfer.
• Linguistically-motivated steps enhance translation accuracy.

Method

CoPiT decomposes MT into Traditional-to-Cyrillic pivoting (morphological segmentation, multi-step script disambiguation, self-reflection) then Cyrillic-to-target translation, using prompt-tuned LLMs.

In practice

• Generate synthetic parallel data from Traditional-script text.
• Improve Traditional Mongolian to target language translation.
• Enable target language to Traditional Mongolian translation.

Topics

• Machine Translation
• Low-Resource Languages
• Mongolian Language
• Digraphic Scripts
• Pivot Translation
• Script Disambiguation
• Synthetic Data Generation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.