Medical Expertise Style Transfer using Denoising Autoencoder

Abstract

Due to the huge cognitive bias and the curse of knowledge, there is a notable communication gap between experts and laymen. This communication gap creates a huge problem in the medical domain. The patients do not understand what the doctors (domain expert) are saying and the doctors also face some ambiguity issues since they are not used to the laymen style. Bridging the gap between laymen and experts is a challenging task as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. To bridge the gap between doctors and patients, we proposed a new approach of text style transfer for non-parallel data. Our proposed approach is based on masking expert terms and denoising autoencoder. We trained and tested our approach on MSD dataset and achieved a stable score across content similarity, perplexity, and style accuracy metrics