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2022 EMNLP EMNLP 2022

Findings of the 2022 Conference on Machine Translation (WMT22)

Abstract

AbstractThis paper presents the results of the General Machine Translation Task organised as part of the Conference on Machine Translation (WMT) 2022. In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of four different domains. We evaluate system outputs with human annotators using two different techniques: reference-based direct assessment and (DA) and a combination of DA and scalar quality metric (DA+SQM).

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — conference evaluation
🐣 Hot Topic Early Bird — multilingual natural language processing
🐝 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, Robotics, Security & Privacy, Speech & Audio

Authors

Tom Kocmi , Rachel Bawden , Ondřej Bojar , Anton Dvorkovich , Christian Federmann , Mark Fishel , Thamme Gowda , Yvette Graham , Roman Grundkiewicz , Barry Haddow , Rebecca Knowles , Philipp Koehn , Christof Monz , Makoto Morishita , Masaaki Nagata , Toshiaki Nakazawa , Michal Novák , Martin Popel , Maja Popović

Topics

Natural Language Processing > Applications > Machine Translation Machine Learning > Learning Types > Machine Translation

Keywords

machine translation multilingual translation neural machine translation human evaluation translation quality multilingual natural language processing direct assessment conference evaluation
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