From Physician Queries to Logical Forms for Efficient Exploration of Patient Data

摘要

We introduce a new question answering paradigm in which users can interact with the system using natural language questions or direct actions within a graphical user interface (GUI). The system displays multiple time series characterizing the behavior of a patient, and a physician interacts with the system through GUI actions and questions, where answers may depend on previous interactions. To find the answers automatically, we propose parsing the questions into logical forms for execution by an inference engine over the underlying database. The semantic parser is implemented as an LSTM-based encoder-decoder that models dependencies between consecutive answers through multiple attention and copying mechanisms. To train and evaluate the model, we created a dataset of semantic parses of real interactions with the system, augmented with a larger dataset of artificial interactions. The proposed architecture obtains promising results, substantially outperforming standard sequence generation baselines.