  Quality estimation aims to measure the quality of translated content without
access to a reference translation. This is crucial for machine translation
systems in real-world scenarios where high-quality translation is needed. While
many approaches exist for quality estimation, they are based on supervised
machine learning requiring costly human labelled data. As an alternative, we
propose a technique that does not rely on examples from human-annotators and
instead uses synthetic training data. We train off-the-shelf architectures for
supervised quality estimation on our synthetic data and show that the resulting
models achieve comparable performance to models trained on human-annotated
data, both for sentence and word-level prediction.
