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Findings of the WMT 2020 shared task on quality estimation
Specia, Lucia Blain, Frédéric Fomicheva, Marina Fonseca, Erick Chaudhary, Vishrav Guzmán, Francisco Martins, André FT
Specia, Lucia
Blain, Frédéric
Fomicheva, Marina
Fonseca, Erick
Chaudhary, Vishrav
Guzmán, Francisco
Martins, André FT
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2020-11-30
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Abstract
We report the results of the WMT20 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word, sentence and document levels. This edition included new data with open domain texts, direct assessment annotations, and multiple language pairs: English-German, English-Chinese, Russian-English, Romanian-English, Estonian-English, Sinhala-English and Nepali-English data for the sentence-level subtasks, English-German and English-Chinese for the word-level subtask, and English-French data for the document-level subtask. In addition, we made neural machine translation models available to participants. 19 participating teams from 27 institutions submitted altogether 1374 systems to different task variants and language pairs.
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Specia, L., Blain, F., Fomicheva, M. et al. (2020) Findings of the WMT 2020 shared task on quality estimation, Proceedings of the Fifth Conference on Machine Translation, November 2020, pp. 743–764.
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en
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© 2020 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website; https://www.aclweb.org/anthology/2020.wmt-1.79
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9781948087810