Search like an expert: Reducing expertise disparity using a hybrid neural index for COVID-19 queries

Consumers from non-medical backgrounds often look for information regarding a specific medical information need; however, they are limited by their lack of medical knowledge and may not be able to find reputable resources. As a case study, we investigate reducing this knowledge barrier to allow consumers to achieve search effectiveness comparable to that of an expert, or a medical professional, for COVID-19 related questions. We introduce and evaluate a hybrid index model that allows a consumer to formulate queries using consumer language to find relevant answers to COVID-19 questions. Our aim is to reduce performance degradation between medical professional queries and those of a consumer. We use a universal sentence embedding model to project consumer queries into the same semantic space as professional queries. We then incorporate sentence embeddings into a search framework alongside an inverted index. Documents from this index are retrieved using a novel scoring function that considers sentence embeddings and BM25 scoring. We find that our framework alleviates the expertise disparity, which we validate using an additional set of crowdsourced-consumer-queries even in an unsupervised setting. We also propose an extension of our method, where the sentence encoder is optimised in a supervised setup. Our framework allows for a consumer to search using consumer queries to match the search performance with that of a professional.