  Most studies on word-level Quality Estimation (QE) of machine translation
focus on language-specific models. The obvious disadvantages of these
approaches are the need for labelled data for each language pair and the high
cost required to maintain several language-specific models. To overcome these
problems, we explore different approaches to multilingual, word-level QE. We
show that these QE models perform on par with the current language-specific
models. In the cases of zero-shot and few-shot QE, we demonstrate that it is
possible to accurately predict word-level quality for any given new language
pair from models trained on other language pairs. Our findings suggest that the
word-level QE models based on powerful pre-trained transformers that we propose
in this paper generalise well across languages, making them more useful in
real-world scenarios.
