ECALP: Empirical Computational Argumentation in Legal Proceedings

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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.