  Clinical semantic parsing (SP) is an important step toward identifying the
exact information need (as a machine-understandable logical form) from a
natural language query aimed at retrieving information from electronic health
records (EHRs). Current approaches to clinical SP are largely based on
traditional machine learning and require hand-building a lexicon. The recent
advancements in neural SP show a promise for building a robust and flexible
semantic parser without much human effort. Thus, in this paper, we aim to
systematically assess the performance of two such neural SP models for EHR
question answering (QA). We found that the performance of these advanced neural
models on two clinical SP datasets is promising given their ease of application
and generalizability. Our error analysis surfaces the common types of errors
made by these models and has the potential to inform future research into
improving the performance of neural SP models for EHR QA.
