  In the U.S. Congress, legislators can use active and passive cosponsorship to
support bills. We show that these two types of cosponsorship are driven by two
different motivations: the backing of political colleagues and the backing of
the bill's content. To this end, we develop an Encoder+RGCN based model that
learns legislator representations from bill texts and speech transcripts. These
representations predict active and passive cosponsorship with an F1-score of
0.88. Applying our representations to predict voting decisions, we show that
they are interpretable and generalize to unseen tasks.
