ECALP: Empirical Computational Argumentation in Legal Proceedings

ECALP：法律诉讼中的经验计算论证

• 批准号: 470110829
• 负责人: Professor Dr. Christoph Burchard
• 金额: --
• 依托单位: Fachgebiet Ubiquitäre Wissensverarbeitung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/470110829?language=en
• 关键词: ECALP; Empirical; Computational; Argumentation; Legal

This project aims to contribute to the foundations of computational argumentation in legal proceedings in these methodology-oriented areas: (1) robust argument mining in the challenging context of a "small-data" scenario and complex legal domain, and (2) scalable methodologies for data acquisition that require expert knowledge. To do so, we tackle the following three research questions (RQ): RQ1: Foundations. How to annotate, model, and mine legal arguments and argumentation in legal proceedings? RQ2: Contextualization. How to model and recognize similar argumentation patterns in legal proceedings? RQ3: Insights. What is the role of oral and written arguments at human rights courts? These research questions are tackled in three consecutive phases. The first phase is devoted to data acquisition and annotation, the second phase to modeling and empirical analysis, and the third phase to research on argument importance and argument transfer across different legal systems. Within the field of computational argumentation, we identify two key methodological directions where progress in NLP will empower empirical research on legal proceedings - namely, neural approaches that can be deployed and transferred efficiently and Bayesian approaches that provide proper model confidences for expert assessment. We also envision contributions to empirical legal research (such as contrasting theories of legal argumentation models with empirical evidence) and an outreach to the wider natural language processing community through a shared task.

该项目旨在为以下以方法论为导向的领域的法律诉讼中的计算论证奠定基础：（1）在“小数据”场景和复杂法律领域的挑战性背景下进行稳健的论证挖掘，以及（2）需要专业知识的可扩展数据采集方法。为此，我们解决以下三个研究问题 (RQ)： RQ1：基础。如何对法律诉讼中的法律论证和论证进行注释、建模和挖掘？ RQ2：情境化。如何在法律诉讼中建模和识别类似的论证模式？ RQ3：见解。人权法庭的口头和书面辩论有何作用？这些研究问题分三个连续阶段解决。第一阶段致力于数据获取和注释，第二阶段致力于建模和实证分析，第三阶段研究论点重要性和跨不同法律体系的论点转移。在计算论证领域，我们确定了两个关键的方法论方向，NLP 的进展将赋能法律诉讼的实证研究，即可以有效部署和转移的神经方法，以及为专家评估提供适当模型置信度的贝叶斯方法。我们还设想对实证法律研究做出贡献（例如将法律论证模型的理论与实证证据进行对比），并通过共同任务扩展到更广泛的自然语言处理社区。