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