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Mapping and translating professional but arcane clinical jargons to consumerlanguage is essential to improve the patient-clinician communication.Researchers have used the existing biomedical ontologies and consumer healthvocabulary dictionary to translate between the languages. However, suchapproaches are limited by expert efforts to manually build the dictionary,which is hard to be generalized and scalable. In this work, we utilized theembeddings alignment method for the word mapping between unparalleled clinicalprofessional and consumer language embeddings. To map semantically similarwords in two different word embeddings, we first independently trained wordembeddings on both the corpus with abundant clinical professional terms and theother with mainly healthcare consumer terms. Then, we aligned the embeddings bythe Procrustes algorithm. We also investigated the approach with theadversarial training with refinement. We evaluated the quality of the alignmentthrough the similar words retrieval both by computing the model precision andas well as judging qualitatively by human. We show that the Procrustesalgorithm can be performant for the professional consumer language embeddingsalignment, whereas adversarial training with refinement may find some relationsbetween two languages.

Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment