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arXiv · 2509.05425

No Text Needed: Forecasting MT Quality and Inequity from Fertility and Metadata

Jessica M. Lundin·Ada Zhang·David Adelani·Cody Carroll
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Abstract

We show that translation quality can be predicted with surprising accuracy \textit{without ever running the translation system itself}. Using only a handful of features, token fertility ratios, token counts, and basic linguistic metadata (language family, script, and region), we can forecast ChrF scores for GPT-4o translations across 203 languages in the FLORES-200 benchmark. Gradient boosting models achieve favorable performance ($R^{2}=0.66$ for XX$\rightarrow$English and $R^{2}=0.72$ for English$\rightarrow$XX). Feature importance analyses reveal that typological factors dominate predictions into English, while fertility plays a larger role for translations into diverse target languages. These findings suggest that translation quality is shaped by both token-level fertility and broader linguistic typology, offering new insights for multilingual evaluation and quality estimation.

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Jessica M. Lundin, Ada Zhang, David Adelani, Cody Carroll. 2026-03-03. No Text Needed: Forecasting MT Quality and Inequity from Fertility and Metadata. https://arxiv.org/abs/2509.05425

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