Joint Representation Learning of Legislator and Legislation for Roll Call Prediction
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1424-1430. https://doi.org/10.24963/ijcai.2020/198
In this paper, we explore to learn representations of legislation and legislator for the prediction of roll call results. The most popular approach for this topic is named the ideal point model that relies on historical voting information for representation learning of legislators. It largely ignores the context information of the legislative data. We, therefore, propose to incorporate context information to learn dense representations for both legislators and legislation. For legislators, we incorporate relations among them via graph convolutional neural networks (GCN) for their representation learning. For legislation, we utilize its narrative description via recurrent neural networks (RNN) for representation learning. In order to align two kinds of representations in the same vector space, we introduce a triplet loss for the joint training. Experimental results on a self-constructed dataset show the effectiveness of our model for roll call results prediction compared to some state-of-the-art baselines.
Data Mining: Mining Text, Web, Social Media
Natural Language Processing: NLP Applications and Tools