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.
翻译:目前的医疗问题解答系统难以处理病人提交的长期、详细和非正式的、称为消费者健康问题(CHQs)的问题。为了解决这一问题,我们引入了一个医学问题理解和回答系统,该系统具有知识基础和语义自我监督。我们的系统是一个管道,它首先利用监督的总结损失,汇总一个长的、医学的、用户写的问题。然后,我们的系统进行两步检索,以回复答案。系统首先从一个可信赖的医疗知识库中将汇总的用户问题与一个常见的常见问题匹配,然后从相应的回答文件中检索固定数量的相关句子。在没有匹配或回答相关性的标签的情况下,我们设计了3个新颖的、自我监督的和语义指导的损失。我们用两个强有力的检索回答基线的问题来评估我们的模型。评估人员根据他们自己的问题,根据他们的相关性来评估我们基线和自己的系统所检索的答案。他们发现我们的系统检索了更相关的答案,同时速度更快了20倍。我们的自我检查损失还帮助总经理在ROUGE中取得更高的分数,作为人类衡量标准。