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Current medical question answering systems have difficulty processing long,detailed and informally worded questions submitted by patients, called ConsumerHealth Questions (CHQs). To address this issue, we introduce a medical questionunderstanding and answering system with knowledge grounding and semanticself-supervision. Our system is a pipeline that first summarizes a long,medical, user-written question, using a supervised summarization loss. Then,our system performs a two-step retrieval to return answers. The system firstmatches the summarized user question with an FAQ from a trusted medicalknowledge base, and then retrieves a fixed number of relevant sentences fromthe corresponding answer document. In the absence of labels for questionmatching or answer relevance, we design 3 novel, self-supervised andsemantically-guided losses. We evaluate our model against two strongretrieval-based question answering baselines. Evaluators ask their ownquestions and rate the answers retrieved by our baselines and own systemaccording to their relevance. They find that our system retrieves more relevantanswers, while achieving speeds 20 times faster. Our self-supervised lossesalso help the summarizer achieve higher scores in ROUGE, as well as in humanevaluation metrics. We release our code to encourage further research.

Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision