  Text simplification systems generate versions of texts that are easier to
understand for a broader audience. The quality of simplified texts is generally
estimated using metrics that compare to human references, which can be
difficult to obtain. We propose Simple-QE, a BERT-based quality estimation (QE)
model adapted from prior summarization QE work, and show that it correlates
well with human quality judgments. Simple-QE does not require human references,
which makes the model useful in a practical setting where users would need to
be informed about the quality of generated simplifications. We also show that
we can adapt this approach to accurately predict the complexity of
human-written texts.
