  Predicting how Congressional legislators will vote is important for
understanding their past and future behavior. However, previous work on
roll-call prediction has been limited to single session settings, thus did not
consider generalization across sessions. In this paper, we show that metadata
is crucial for modeling voting outcomes in new contexts, as changes between
sessions lead to changes in the underlying data generation process. We show how
augmenting bill text with the sponsors' ideologies in a neural network model
can achieve an average of a 4% boost in accuracy over the previous
state-of-the-art.
