Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision
Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision
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DOI：
10.48550/arxiv.2209.15301
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发表时间：
2022-09
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影响因子：
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通讯作者：
Khalil Mrini;Harpreet Singh;Franck Dernoncourt;Seunghyun Yoon;Trung Bui;Walter Chang;E. Farcas;Ndapandula Nakashole
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作者：
Khalil Mrini;Harpreet Singh;Franck Dernoncourt;Seunghyun Yoon;Trung Bui;Walter Chang;E. Farcas;Ndapandula Nakashole
目前的医学问答系统很难处理患者提交的冗长、详细和非正式的问题，称为消费者健康问题(CHQ)。为了解决这一问题，我们引入了一个基于知识和语义自我监督的医学问题理解和回答系统。我们的系统是一个管道，它首先使用监督的摘要丢失来摘要一个冗长的、医学的、用户编写的问题。然后，我们的系统执行两步检索以返回答案。该系统首先将总结的用户问题与来自可信医学知识库的FAQ进行匹配，然后从相应的回答文档中检索固定数量的相关句子。在没有用于问题匹配或答案相关性的标签的情况下，我们设计了3个新颖的、自我监督的和语义引导的损失。我们根据两条强的基于检索的问答基线对我们的模型进行了评估。评估者提出自己的问题，并根据相关性对我们的基线和自己的系统检索到的答案进行评级。他们发现，我们的系统检索更相关的答案，同时速度快20倍。我们的自我监督损失也帮助摘要者在Rouge以及人类评估指标中获得更高的分数。
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.