We present the second annual Shared Task for Creole Language Machine Translation, the only published shared task specializing in machine translation (MT) methods for Creole languages and their communities, presented at the Conference of Machine Translation (WMT) 2026. We accepted system submissions for two tasks: MT and language identification (LID). We received submissions from eight teams: 13 MT systems from six teams across 88 language directions; and four LID systems from two teams, identifying 11 languages each. Our analysis of MT systems indicates fine-tuning with newly curated datasets and/or synthetic data as an effective strategy for producing state-of-the-art MT. For the LID task, we found that smaller, resource-efficient systems achieved state-of-the-art scores on some languages, especially when paired with data augmentation.

Efforts towards better machine translation (MT) for Creole languages have historically been isolated, due to Creole languages’ geographic and linguistic diversity. However, most speakers of Creole languages stand to benefit from improved MT for low-resource languages. To galvanize collaboration for Creole MT across the NLP community, we introduce the First Shared Task for Creole Language Machine Translation at WMT25. This Shared Task consists of two systems tracks and one data track, for which we received submissions from five participating teams. Participants experimented with a wide variety of systems development techniques. Our evaluation campaign gave rise to improvements in MT performance in several languages, and particularly large improvements in new testing genres, though some participants found that reusing subsets of pretraining data for specialized post-training did not yield significant improvements. Our campaign also yielded new test sets for Mauritian Creole and a vast expansion of public training data for two Creole languages of Latin America.