UPPAM: A Unified Pre-training Architecture for Political Actor Modeling based on Language

Modeling political actors is at the core of quantitative political science. Existing works have incorporated contextual information to better learn the representation of political actors for specific tasks through graph models. However, they are limited to the structure and objective of training settings and can not be generalized to all politicians and other tasks. In this paper, we propose a Unified Pre-training Architecture for Political Actor Modeling based on language (UPPAM). In UPPAM, we aggregate statements to represent political actors and learn the mapping from languages to representation, instead of learning the representation of particular persons. We further design structureaware contrastive learning and behavior-driven contrastive learning tasks, to inject multidimensional information in the political context into the mapping. In this framework, we can profile political actors from different aspects and solve various downstream tasks. Experimental results demonstrate the effectiveness and capability of generalization of our method. * Corresponding author. • No church needs to provide contraception under ObamaCare. • Recovery package must provide state aid, hazard pay. • LGBT rights are in jeopardy from Supreme Court. • I oppose school busing because it fails, not for racism. languages social network behaviors Voted NO on defining unborn child as eligible for SCHIP. Voted NO on constitutional ban of same-sex marriage. Voted YES on Educational Savings Accounts.