Entity-Enriched Neural Models for Clinical Question Answering
arxiv(2020)
摘要
We explore state-of-the-art neural models for question answering on electronic medical records and improve their ability to generalize better on previously unseen (paraphrased) questions at test time. We enable this by learning to predict logical forms as an auxiliary task along with the main task of answer span detection. The predicted logical forms also serve as a rationale for the answer. Further, we also incorporate medical entity information in these models via the ERNIE architecture. We train our models on the large-scale emrQA dataset and observe that our multi-task entity-enriched models generalize to paraphrased questions ~5% better than the baseline BERT model.
更多查看译文
关键词
clinical,neural models,entity-enriched
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络