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STAR metric and StarPO framework improve document-to-document machine translation alignment

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Researchers introduce Sentence Translation Alignment Rate (STAR), an auxiliary metric designed to quantify sentence-level structural fidelity in document-to-document machine translation, addressing issues like omissions and hallucinations. They propose STAR-masked Preference Optimization (StarPO), a framework that ranks hypotheses by structural quality and uses a dynamic alignment mask to focus optimization on misaligned segments.

STAR explicitly measures structural fidelity to address source-target correspondence violations.
StarPO utilizes a dynamic alignment mask to optimize specific misaligned segments during training.
Experiments across news and literary domains show significant enhancements in translation quality and integrity.
Compact models using StarPO surpass the performance of massive proprietary systems like GPT-4o with superior token efficiency.

The framework allows smaller models to outperform large proprietary systems while maintaining better token efficiency.

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