1 research outputs found
Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision
Current medical question answering systems have difficulty processing long,
detailed and informally worded questions submitted by patients, called Consumer
Health Questions (CHQs). To address this issue, we introduce a medical question
understanding and answering system with knowledge grounding and semantic
self-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 first
matches the summarized user question with an FAQ from a trusted medical
knowledge base, and then retrieves a fixed number of relevant sentences from
the corresponding answer document. In the absence of labels for question
matching or answer relevance, we design 3 novel, self-supervised and
semantically-guided losses. We evaluate our model against two strong
retrieval-based question answering baselines. Evaluators ask their own
questions and rate the answers retrieved by our baselines and own system
according to their relevance. They find that our system retrieves more relevant
answers, while achieving speeds 20 times faster. Our self-supervised losses
also help the summarizer achieve higher scores in ROUGE, as well as in human
evaluation metrics. We release our code to encourage further research.Comment: Accepted as Main Conference Long paper at COLING 202