Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer

Understand

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.