AI RESEARCH
Translation or Recitation? Calibrating Evaluation Scores for Machine Translation of Extremely Low-Resource Languages
arXiv CS.LG
•
ArXi:2603.25222v1 Announce Type: cross 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., native African or American languages) result from superior methodologies or are merely artifacts of benchmark collection. To address this problem, we.