Deep Ordinal Regression for Pledge Specificity Prediction
This work addresses the lack of automated tools for political analysis of campaign pledges, benefiting researchers and analysts, but it is incremental as it applies existing deep learning techniques to a new domain-specific dataset.
The paper tackled the problem of predicting pledge specificity in political manifestos by creating a novel dataset of over 12,000 annotated sentences from Australian elections and proposing deep ordinal regression methods, which outperformed baseline approaches in empirical results.
Many pledges are made in the course of an election campaign, forming important corpora for political analysis of campaign strategy and governmental accountability. At present, there are no publicly available annotated datasets of pledges, and most political analyses rely on manual analysis. In this paper we collate a novel dataset of manifestos from eleven Australian federal election cycles, with over 12,000 sentences annotated with specificity (e.g., rhetorical vs.\ detailed pledge) on a fine-grained scale. We propose deep ordinal regression approaches for specificity prediction, under both supervised and semi-supervised settings, and provide empirical results demonstrating the effectiveness of the proposed techniques over several baseline approaches. We analyze the utility of pledge specificity modeling across a spectrum of policy issues in performing ideology prediction, and further provide qualitative analysis in terms of capturing party-specific issue salience across election cycles.