Findings of the WMT 2025 Shared Task of the Open Language Data Initiative (2025)

Abstract: We present the results of the WMT 2025 shared task of the Open Language Data Initiative. Participants were invited to contribute to the massively multilingual open datasets (FLORES+, MT Seed, WMT24++) or create new such resources. We accepted 8 submissions, including 7 extensions or revisions of the existing datasets and one submission with a new parallel training dataset, SMOL.

## introduction recent advances in machine translation (mt) have resulted in system output which is indistinguishable from that of human translators @xcite . however, even if we assume that the task of mt is 'solved', this would be true only for a small number of well-resourced language pairs. achieving such high task performance requires abundant parallel training data specific to the language pair and domain of interest, either as explicit parallel datasets, or as incidental bilingual signals in generic web data @xcite . for the majority of the world's languages, we lack both the parallel training data necessary to train mt models, as well as the evaluation data to assess their translation capabilities. one way to address this bottleneck is creation of massively multilingual parallel datasets and their extension to new languages. in this paper, we * equal contribution describe the second shared task for open language data initiative (oldi) which invited language communities to contribute to high-quality, massively parallel and open-source datasets. contributions could involve either extending existing datasets to new language varieties, making substantial improvements to existing datasets, or creating new massively multilingual parallel datasets. the datasets of interest to the shared task include (but are not limited to) flores+, oldi-seed @xcite , and wmt24++ @xcite . the oldi itself is a community of researchers that maintains the former two datasets and promotes better language resources for under-served languages in general. 1 this year, we received eight submissions, including six extensions of datasets to new language varieties, two revisions of existing translations, and one entirely new massively parallel dataset. all the data will be made available online under permissive open-source licenses. foot_1 2 datasets 2.1 flores+ flores is a family of datasets designed to benchmark multilingual translation, with many-to-many alignment across over 200 languages. the first iteration of this dataset covered only three languages @xcite , but following iterations increased coverage first to 101 languages (flores-101, @xcite and then to over 200 languages as part of the "no language left behind" project @xcite . finally, as part of the previous edition of this shared task, an additional 8 languages were included on top of several corrections to existing datasets @xcite @xcite @xcite @xcite . this new, living version of the flores benchmark is released under the name flores+. ## shared task definition the goal of the shared task was to expand high-quality, massively-parallel and open-source datasets to improve the resources available for multilingual applications like mt. contributions could consist of the addition of new language varieties to existing datasets, substantial improvements to existing datasets, or novel datasets compatible with the aims of oldi. most contributions were to the datasets managed by oldi: flores+ and oldi-seed. ## conclusion we presented the results of the wmt 2025 oldi shared task. we accepted 8 submissions covering 16 languages, including the new smol dataset covering 123 languages, and extensions or revisions of the existing foundational datasets, flores+, oldi-seed, and wmt24++, in 14 language varieties. we are truly grateful to all participants for their work and we hope that these contributions are soon adopted by the research community, enhancing a positive feedback loop between the developers of language technologies and the communities of language speakers.