@inproceedings{e5fa034c91624fa9b668947799c5da68,
title = "BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task",
author = "Marina Fomicheva and Shuo Sun and Lisa Yankovskaya and Fr{\'e}d{\'e}ric Blain and Vishrav Chaudhary and Mark Fishel and Francisco Guzm{\'a}n and Lucia Specia",
note = "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.",
year = "2020",
language = "English",
booktitle = "Proceedings of the Quality Estimation Shared task at WMT 2020",
publisher = "Association for Computational Linguistics",
}