This 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.

We introduce a Spanish-Awajun parallel dataset of 22k high-quality sentence pairs with the help of the journalistic organization Company C. This dataset consists of parallel data obtained from various web sources such as poems, stories, laws, protocols, guidelines, handbooks, the Bible, and news published by Company C. The study also includes an analysis of the dataset’s performance for Spanish-Awajun translation using a Transformer architecture with transfer learning from a parent model, utilizing Spanish-English and Spanish-Finnish as high-resource language-pairs. As far as we know, this is the first Spanish-Awajun machine translation study, and we hope that this work will serve as a starting point for future research on this neglected Peruvian language.

We present the submission of REPUcs to the AmericasNLP machine translation shared task for the low resource language pair Spanish–Quechua. Our neural machine translation system ranked first in Track two (development set not used for training) and third in Track one (training includes development data). Our contribution is focused on: (i) the collection of new parallel data from different web sources (poems, lyrics, lexicons, handbooks), and (ii) using large Spanish–English data for pre-training and then fine-tuning the Spanish–Quechua system. This paper describes the new parallel corpora and our approach in detail.