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Predicting how Congressional legislators will vote is important forunderstanding their past and future behavior. However, previous work onroll-call prediction has been limited to single session settings, thus did notconsider generalization across sessions. In this paper, we show that metadatais crucial for modeling voting outcomes in new contexts, as changes betweensessions lead to changes in the underlying data generation process. We show howaugmenting bill text with the sponsors' ideologies in a neural network modelcan achieve an average of a 4% boost in accuracy over the previousstate-of-the-art.

Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Prediction