Entity-Enriched Neural Models for Clinical Question Answering
This work addresses generalization challenges in clinical QA for healthcare applications, but it is incremental as it builds on existing neural models and datasets.
The paper tackled the problem of improving generalization to paraphrased questions in clinical question answering on electronic medical records by using multi-task learning with logical form prediction and medical entity information, resulting in a ~5% better generalization than the baseline BERT model.
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.