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BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task
Fomicheva, Marina Sun, Shuo Yankovskaya, Lisa Blain, Frédéric Chaudhary, Vishrav Fishel, Mark Guzmán, Francisco Specia, Lucia
Fomicheva, Marina
Sun, Shuo
Yankovskaya, Lisa
Blain, Frédéric
Chaudhary, Vishrav
Fishel, Mark
Guzmán, Francisco
Specia, Lucia
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2020-11-30
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Abstract
This paper presents our submission to the WMT2020 Shared Task on Quality Estimation (QE). We participate in Task and Task 2 focusing on sentence-level prediction. We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glass-box approaches that leverage various indicators that can be extracted from the neural MT systems. In addition to training a feature-based regression model using glass-box quality indicators, we also test whether they can be used to predict MT quality directly with no supervision. We assess our systems in a multi-lingual setting and show that both types of approaches generalise well across languages. Our black-box QE models tied for the winning submission in four out of seven language pairs inTask 1, thus demonstrating very strong performance. The glass-box approaches also performed competitively, representing a light-weight alternative to the neural-based models.
Citation
Fomicheva, M., Sun, S., Yankovskaya, L. et al. (2020) BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task, Proceedings of the Fifth Conference on Machine Translation, November 2020, pp. 1010–1017.
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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-1.116/
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9781948087810