Neural Ideal Point Estimation Network

TL;DR

The paper introduces NIPEN, a novel neural network model that combines deep learning and probabilistic graphical models to analyze legislative data, capturing latent factors and improving voting prediction accuracy.

Contribution

NIPEN is a new model that effectively learns low-dimensional representations of legislative texts and voter behavior, enhancing understanding of political dynamics.

Findings

NIPEN accurately predicts legislators' votes.

NIPEN uncovers hidden trust networks among legislators.

The model captures complex causal relationships in legislative data.

Abstract

Understanding politics is challenging because the politics take the influence from everything. Even we limit ourselves to the political context in the legislative processes; we need a better understanding of latent factors, such as legislators, bills, their ideal points, and their relations. From the modeling perspective, this is difficult 1) because these observations lie in a high dimension that requires learning on low dimensional representations, and 2) because these observations require complex probabilistic modeling with latent variables to reflect the causalities. This paper presents a new model to reflect and understand this political setting, NIPEN, including factors mentioned above in the legislation. We propose two versions of NIPEN: one is a hybrid model of deep learning and probabilistic graphical model, and the other model is a neural tensor model. Our result indicates that NIPEN successfully learns the manifold of the legislative bill texts, and NIPEN utilizes the learned low-dimensional latent variables to increase the prediction performance of legislators' votings. Additionally, by virtue of being a domain-rich probabilistic model, NIPEN shows the hidden strength of the legislators' trust network and their various characteristics on casting votes.