MARDY: Modeling Argumentation Dynamics in Political Discourse (Phase 2)

这个涉及计算语言学，机器学习和政治学的跨学科合作项目旨在开发用于分析政治话语中的论证的计算模型和方法-特别是随着时间的推移捕获有争议问题的话语交流的动态。目的是支持分析不同政治行为体提出的论点可能产生的影响。在这方面，除了索赔的实质内容及其理由之外，还需要考虑其他因素：话语联盟的发展和结构，论证和框架的重叠和竞争使用，行动者的地位和关系等因素，这些因素将被整合到有效的，可扩展的计算模型，支持专业分析师（如政治学家）和知情的外行研究当前和过去的辩论。我们的建模方法将语言技术的最先进分析方法与强大的机器学习技术（特别是联合推理和深度学习）以及政治学的分析见解相结合，以产生与参与者、主张和理由相关的全球连贯话语网络。我们通过解决从文本中提取上述因素并将其组合到更复杂模型中的必要计算组件任务来实现这一点。作为模型开发的一部分，该项目将为日报报道的德国政治话语中过去辩论的语料库创建高质量的注释（编码），捕捉与问题相关的主张内容，论证结构和相关话语参考。注释的报纸语料库将免费提供给学术研究。为了补充通过报纸调解的辩论的经验性访问，我们将使用其他来源（可用的议会程序语料库）来访问参与者的原始贡献，并对辩论立场进行半结构化的系统化。通过将动态网络模型应用于真实的辩论数据集，回顾性预测建模实验可以用来检验关于驱动政治话语的经验相关因素的假设，并学习如何模型参数涉及理论解释的具体要素和/或专家分析人员通过经验得出的直觉。使用交互式可视化技术和诊断工具，我们将开发的建模，（家庭）应用于新数据的预测模型可以有助于识别正在进行的辩论中的意外转折，并在政治话语中的论证动态的探索性或系统性分析的其他方案。参与小组之间的密切联系确保了计算模型转化为政治科学家的有用工具，我们的目标是在项目过程中为这种交流建立一种“最佳做法”方法。

This interdisciplinary collaboration project involving Computational Linguistics, Machine Learning and Political Science has the aim of developing computational models and methods for analyzing argumentation in political discourse - specifically capturing the dynamics of discursive exchanges on controversial issues over time. The goal is to support analysis of the possible impact of arguments advanced by different political actors. Here, factors besides the substance of the claim and its justification need to be considered: the development and structure of discourse coalitions, overlapping and competing use of justifications and frames, the status of and relations between actors, etc. Such factors are to be integrated into effective, scalable computational models which support both professional analysts (such as political scientists) and informed laypersons researching current and past debates. Our modeling approach combines state-of-the-art analysis methods from language technology with powerful machine learning techniques (in particular joint inference and deep learning) and analytical insights from political science to produce globally coherent discourse networks relating actors, claims, and justifications. We do so by solving the necessary computational component tasks of extracting the above factors from texts and combining them in more complex models. As part of model development, the project will create high-quality annotations (coding) for a corpus of past debates in German political discourse as covered in daily newspapers, capturing issue-related content of claims, argumentative structure and relevant discourse references. The annotated newspaper corpus will be made freely available for academic research. To complement empirical access to debates mediated through newspapers, we will use additional sources (available corpora of parliamentary proceedings) to access actors' original contributions and for semi-structured systematization of argumentative positions.By applying dynamic network models to datasets of real debates, retrospective predictive modeling experiments can be used to test hypotheses about empirically relevant factors driving political discourse and to learn how model parameters relate to specific elements of theoretical interpretation and/or to intuitions that expert analysts have developed through experience. Using interactive visualization techniques and diagnostic tools that we will develop alongside the modeling, (families of) predictive models applied on new data can contribute to the identification of unexpected turns in an ongoing debate, and to other schemes of explorative or systematic analysis of argumentation dynamics in political discourse. The close connection among the participating groups ensures that the computational models transfer into useful tools for political scientists, and we aim at establishing a "best-practice" methodology for this exchange over the course of the project.