BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Vishrav Chaudhary, Mark Fishel, Francisco Guzmán, Lucia Specia · 2020
This paper presents our submission to the WMT2020 Shared Task on Quality Estimation (QE) 1 .We participate in Task 1 and Task 2 focusing on sentence-level prediction.We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glassbox 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 multilingual 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 in Task 1, thus demonstrating very strong performance.The glass-box approaches also performed competitively, representing a lightweight alternative to the neural-based models.