@inproceedings{fomicheva-etal-2020-bergamot,
title = "{BERGAMOT}-{LATTE} Submissions for the {WMT}20 Quality Estimation Shared Task",
author = "Fomicheva, Marina and
Sun, Shuo and
Yankovskaya, Lisa and
Blain, Fr{\'e}d{\'e}ric and
Chaudhary, Vishrav and
Fishel, Mark and
Guzm{\'a}n, Francisco and
Specia, Lucia",
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.116",
pages = "1010--1017",
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.",
}
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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.</abstract>
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%0 Conference Proceedings
%T BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task
%A Fomicheva, Marina
%A Sun, Shuo
%A Yankovskaya, Lisa
%A Blain, Frédéric
%A Chaudhary, Vishrav
%A Fishel, Mark
%A Guzmán, Francisco
%A Specia, Lucia
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F fomicheva-etal-2020-bergamot
%X 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.
%U https://aclanthology.org/2020.wmt-1.116
%P 1010-1017
Markdown (Informal)
[BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task](https://aclanthology.org/2020.wmt-1.116) (Fomicheva et al., WMT 2020)
ACL