  We describe a new semantic parsing setting that allows users to query the
system using both natural language questions and actions within a graphical
user interface. Multiple time series belonging to an entity of interest are
stored in a database and the user interacts with the system to obtain a better
understanding of the entity's state and behavior, entailing sequences of
actions and questions whose answers may depend on previous factual or
navigational interactions. We design an LSTM-based encoder-decoder architecture
that models context dependency through copying mechanisms and multiple levels
of attention over inputs and previous outputs. When trained to predict tokens
using supervised learning, the proposed architecture substantially outperforms
standard sequence generation baselines. Training the architecture using policy
gradient leads to further improvements in performance, reaching a
sequence-level accuracy of 88.7% on artificial data and 74.8% on real data.
