Translation or Recitation? Calibrating Evaluation Scores for Machine Translation of Extremely Low-Resource Languages

ACL ARR 2026 January Submission2108 Authors

Keywords: low reosurce machine translation dataset analysis

Abstract: 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 \textbf{FRED Difficulty Metrics}—\textit{Fertility Ratio (F)}, \textit{Retrieval Proxy} ($R$) \textit{Pre-training Exposure} ($E$) and \textit{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.

Paper Type: Short

Research Area: Low-resource Methods for NLP

Research Area Keywords: low resource languages machine translation data contamination

Contribution Types: Model analysis & interpretability, Approaches to low-resource settings, Publicly available software and/or pre-trained models, Data analysis

Languages Studied: akkadian,ancient egytian,ancient language,indigenous langauge,americas language,african language, indic language

Submission Number: 2108