论文标题
临床问题的富含实体的神经模型回答
Entity-Enriched Neural Models for Clinical Question Answering
论文作者
论文摘要
我们探索了最新的神经模型，以回答有关电子病历的问题，并在测试时提高了他们在以前看不见的（释义）问题上更好地推广其能力。我们可以通过学习将逻辑形式作为辅助任务以及答案跨度检测的主要任务来实现这一目标。预测的逻辑形式也是答案的理由。此外，我们还通过Ernie Architecture将医疗实体信息纳入了这些模型。我们在大型EMRQA数据集上训练我们的模型，并观察到，我们多任务实体添加的模型概括以释义比基线BERT模型好5％。
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.
