Abstract

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 and 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.

Bio

Nate Robinson is a second-year PhD student at Johns Hopkins University’s Center for Language and Speech Processing. He is advised by Kenton Murray and Sanjeev Khudanpur. He completed his Masters in Language Technologies at Carnegie Mellon University under the advisement of David Mortensen, and he researched at Brigham Young University’s DRAGN Labs under Nancy Fulda prior to that. Nate’s research focuses on building language technologies, including machine translation and speech technologies, for low-resource and related languages. He is proficient in both English, French, Spanish, Arabic, and Haitian through years of study. He is a nerd about linguistics and languages, as well as math, books, music, natural sciences, and world cultures.