论文标题
在美国国会中解开主动和被动的共同赞助
Disentangling Active and Passive Cosponsorship in the U.S. Congress
论文作者
论文摘要
在美国国会中，立法者可以使用积极和被动的共同赞助来支持法案。我们表明，这两种类型的同事制度是由两种不同的动机驱动的：政治同事的支持和对法案内容的支持。为此，我们开发了一个基于编码器+RGCN的模型，该模型从法案文本和语音笔录中学习立法者表示。这些表示形式可以预测主动和被动的同事制度，F1得分为0.88。应用我们的代表来预测投票决定，我们表明它们是可以解释的，并且可以概括地看不见的任务。
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.
