Medical Expertise Style Transfer using Denoising Autoencoder

Irbaz, Mohammad Sabik; Azad, Abir; Preoty, Anika Tasnim; Shalanyuy, Tani Barkat

dc.contributor.author	Irbaz, Mohammad Sabik
dc.contributor.author	Azad, Abir
dc.contributor.author	Preoty, Anika Tasnim
dc.contributor.author	Shalanyuy, Tani Barkat
dc.date.accessioned	2022-04-20T17:43:38Z
dc.date.available	2022-04-20T17:43:38Z
dc.date.issued	2021-03-15
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dc.identifier.uri	http://hdl.handle.net/123456789/1372
dc.description	Supervised by Md. Kamrul Hasan, Ph.D. Professor, Department of CSE, System and Software Lab (SSL), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.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	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.subject	Text Style Transfer, Transformer, Deep Learning, Denoising Autoencoder, BERT	en_US
dc.title	Medical Expertise Style Transfer using Denoising Autoencoder	en_US
dc.type	Thesis	en_US