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.

本研究提出知识库同化预训练任务和利用较大规模的平行语料库的转换模型，用于增强医学领域专家 - 外行人文本的风格转换系统，从而提高专家与百姓之间的沟通。实验证明，该方法可使转换质量平均达到成功率的 106% 相对提高。