2025 NAACL NAACL 2025

Findings of the AmericasNLP 2025 Shared Tasks on Machine Translation, Creation of Educational Material, and Translation Metrics for Indigenous Languages of the Americas

Abstract

AbstractThis paper presents the findings of the AmericasNLP 2025 Shared Tasks: (1) machine translation for truly low-resource languages, (2) morphological adaptation for generating educational examples, and (3) developing metrics for machine translation in Indigenous languages. The shared tasks cover 14 diverse Indigenous languages of the Americas. A total of 11 teams participated, submitting 26 systems across all tasks, languages, and models. We describe the shared tasks, introduce the datasets and evaluation metrics used, summarize the baselines and submitted systems, and report our findings.

👥 Mega-Team — 22 authors
🌉 Interdisciplinary Bridge — Interdisciplinary and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — morphological adaptation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio