  Patients with low health literacy usually have difficulty understanding
medical jargon and the complex structure of professional medical language.
Although some studies are proposed to automatically translate expert language
into layperson-understandable language, only a few of them focus on both
accuracy and readability aspects simultaneously in the clinical domain. Thus,
simplification of the clinical language is still a challenging task, but
unfortunately, it is not yet fully addressed in previous work. To benchmark
this task, we construct a new dataset named MedLane to support the development
and evaluation of automated clinical language simplification approaches.
Besides, we propose a new model called DECLARE that follows the human
annotation procedure and achieves state-of-the-art performance compared with
eight strong baselines. To fairly evaluate the performance, we also propose
three specific evaluation metrics. Experimental results demonstrate the utility
of the annotated MedLane dataset and the effectiveness of the proposed model
DECLARE.
