Simple-QE: Better Automatic Quality Estimation for Text Simplification
This work addresses the problem of automatically estimating the quality of text simplification for users and developers, offering a reference-free alternative to existing methods.
This paper introduces Simple-QE, a BERT-based quality estimation model for text simplification that does not require human references. It demonstrates good correlation with human quality judgments and can also predict the complexity of human-written texts.
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.