对比不同架构在政治立场量化中的表现，探索联合预测与混合方法的潜力。
Comparing Architectures for Supervised Political Scaling
- 提出联合预测多个政治立场的模型架构
- 混合方法在准确率上优于传统分类与回归
- 适合政治分析与舆论监测研究者参考
文本缩放是将政治人物置于意识形态量表上的基础任务。为减少人工分析需求，已有多种NLP方法被提出，包括基于分类和回归的方法，虽取得一定成效但也存在局限。本文旨在整合该领域的最新进展，回答两个问题：(a) 通过联合预测而非单独预测各量表，能否提升缩放方法性能？(b) 是否存在介于分类与回归之间的中间路径？实验结果表明，联合建模能有效提高精度，且混合方法在多个数据集上表现更优。
原文摘要 · Abstract (English)
Text scaling, the task of positioning political actors on an ideological scale, is a fundamental task in political analysis. To ease the need for manual analysis, various NLP methods have been proposed for this task, including classification- and regression-based approaches, showing successes as well as limitations. The goal of our paper is to consolidate the state of the art in this area. We ask two questions: (a) Can the performance of scaling methods be improved by predicting scales not individually but jointly? (b) Is there a middle ground between classification and regression?
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