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Findings of the 2021 Conference on Machine Translation (WMT21)

Farhad Akhbardeh; Arkady Arkhangorodsky; Magdalena Biesialska; Ondřej Bojar; Rajen Chatterjee; Vishrav Chaudhary; Marta R. Costa-jussa; Cristina España-Bonet; Angela Fan; Christian Federmann; Markus Freitag; Yvette Graham; Roman Grundkiewicz; Barry Haddow; Leonie Barbara Sabine Harter; Kenneth Heafield; Christopher Homan; Matthias Huck; Kwabena Amponsah-Kaakyire; Jungo Kasai; Daniel Khashabi; Kevin Knight; Tom Kocmi; Philipp Koehn; Nicholas Lourie; Christof Monz; Makoto Morishita; Masaaki Nagata; Ajay Nagesh; Toshiaki Nakazawa; Matteo Negri; Santanu Pal; Allahsera Auguste Tapo; Marco Turchi; Valentin Vydrin; Marcos Zampieri
In: Proceedings of the Sixth Conference on Machine Translation (WMT). Conference on Machine Translation (WMT-2021), November 10-11, Dominican Republic, Pages 1-93, ACL, 2021.


This paper presents the results of the news translation task, the multilingual low-resource translation for Indo-European languages, the triangular translation task, and the automatic post-editing task organised as part of the Conference on Machine Translation (WMT) 2021. In the news task, participants were asked to build machine translation systems for any of 10 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the Similar Language Translation (SLT) task, participants were asked to develop systems to translate between pairs of similar languages from the Dravidian and Romance family as well as French to two similar low-resource Manding languages (Bambara and Maninka). In the Triangular MT translation task, participants were asked to build a Russian to Chinese translator, given parallel data in Russian-Chinese, Russian-English and English-Chinese. In the multilingual low-resource translation for Indo-European languages task, participants built multilingual systems to translate among Romance and North-Germanic languages. The task was designed to deal with the translation of documents in the cultural heritage domain for relatively low-resourced languages. In the automatic post-editing (APE) task, participants were asked to develop systems capable to correct the errors made by an unknown machine translation systems.


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