发表机构
Universität Stuttgart; Universität Bremen（斯图加特大学; 不来梅大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建了1.7万句的德国三政治场域论证标注语料库，发现新闻发布会中领域专业知识依据更常见，还开展了论证段落自动识别试点研究，揭示边界难界定及模型存在确认偏差。
AI 中文摘要
审议过程包含论证的构建与交流，是民主国家政治决策的核心组成部分。然而，论证模式在不同政治场域（如全体会议发言、委员会会议）间存在显著差异。尽管学界对论证研究兴趣浓厚，但针对不同场域间政治论证模式差异的计算分析工作相对匮乏，本研究旨在填补这一空白。首先，我们构建了一个包含17000个句子的语料库，对德国三个政治场域（全体会议发言、委员会会议、新闻发布会）中论证性段落（论点及其依据，含边界与类别）进行标注，且所有文本均围绕COVID-19主题展开。对该语料库的分析发现，与预期相反，基于领域专业知识的依据在新闻发布会中的出现频率高于委员会会议。其次，我们开展了一项自动识别此类论证性段落的试点研究，结果表明论证边界难以精准界定，且模型预测还存在确认偏差问题。
英文摘要
Deliberation, involving the formulation and exchange of arguments, forms an integral part of political decision making in democracies. Argumentation patterns however differ substantially across different political arenas, such as plenary speeches and committee meetings. However, despite a lot of interest in argumentation, there is comparatively little computational work on analyzing differences in patterns of political argumentation between arenas. Our work addresses this research gap. First, we present a 17k-sentence corpus with annotation for argumentative passages (argument and their justifications, both their boundaries and their categories) across three German political arenas (plenary speeches, committee meetings, and press conferences), keeping the topic (COVID-19) constant. Our analysis of the corpus finds that contrary to expectations, justification by domain-specific expertise is more frequent in press conferences than in committee meetings. Second, we present a pilot study on automatically identifying such argumentative passages. The results show that boundaries are hard to pin down, and models predictions additionally suffer from confirmation bias.
CommentsAccepted for publication at KONVENS 2026
