Translation or Recitation? Calibrating Evaluation Scores for Machine Translation of Extremely Low-Resource Languages
Danlu Chen, Ka Sing He, Jiahe Tian, Chenghao Xiao Shanghai University of Finance and Economics, Zhaofeng Wu Massachusetts Institute of Technology, Taylor Berg-Kirkpatrick University of California, San Diego, Freda Shi Vector Institute and University of Waterloo
The landscape of extremely low-resource machine translation (MT) is characterized by perplexing variability in reported performance, often making results across different language pairs difficult to contextualize. For researchers focused on specific language groups—such as ancient languages—it is nearly impossible to determine if breakthroughs reported in other contexts (e.g., African or American languages) result from superior methodologies or are merely artifacts of benchmark collection. To address this, we introduce the FRED Difficulty Metrics—Fertility Ratio (F), Retrieval Proxy (R) Pre-training Exposure (E) and Corpus Diversity (D) —that serve as dataset-intrinsic metrics to contextualize reported scores. Our findings reveal that a significant portion of result variability is explained by train-test overlap and pre-training exposure rather than model capability. Additionally, we identify that underperforming XLR languages—particularly extinct and non-Latin indigenous languages—suffer from poor tokenization coverage (high token fertility), highlighting structural limitations of transfer learning for languages outside pre-trained models’ representation space. By providing these indices alongside performance scores, we enable more transparent evaluation of cross-lingual transfer and provide a more reliable foundation for the XLR MT community.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表