2021
EMNLP
EMNLP 2021
Findings of the 2021 Conference on Machine Translation (WMT21)
Abstract
AbstractThis paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021.In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories. The taskwas also opened up to additional test suites toprobe specific aspects of translation.
👥
Mega-Team
— 36 authors
🌉
Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
🐣
Hot Topic Early Bird
— translation evaluation
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Farhad Akhbardeh
,
Arkady Arkhangorodsky
,
Magdalena Biesialska
,
Ondřej Bojar
,
Rajen Chatterjee
,
Vishrav Chaudhary
,
Marta R. Costa-jussà
,
Cristina España-Bonet
,
Angela Fan
,
Christian Federmann
,
Markus Freitag
,
Yvette Graham
,
Roman Grundkiewicz
,
Barry Haddow
,
Leonie 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