  Expert-layman text style transfer technologies have the potential to improve
communication between members of scientific communities and the general public.
High-quality information produced by experts is often filled with difficult
jargon laypeople struggle to understand. This is a particularly notable issue
in the medical domain, where layman are often confused by medical text online.
At present, two bottlenecks interfere with the goal of building high-quality
medical expert-layman style transfer systems: a dearth of pretrained
medical-domain language models spanning both expert and layman terminologies
and a lack of parallel corpora for training the transfer task itself. To
mitigate the first issue, we propose a novel language model (LM) pretraining
task, Knowledge Base Assimilation, to synthesize pretraining data from the
edges of a graph of expert- and layman-style medical terminology terms into an
LM during self-supervised learning. To mitigate the second issue, we build a
large-scale parallel corpus in the medical expert-layman domain using a
margin-based criterion. Our experiments show that transformer-based models
pretrained on knowledge base assimilation and other well-established
pretraining tasks fine-tuning on our new parallel corpus leads to considerable
improvement against expert-layman transfer benchmarks, gaining an average
relative improvement of our human evaluation, the Overall Success Rate (OSR),
by 106%. We release our code and parallel corpus for future research.
