  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.
