LaCour!: enabling research on argumentation in hearings of the European Court of Human Rights

Abstract Why does an argument end up in the final court decision? Was it deliberated or questioned during the oral hearings? Was there something in the hearings that triggered a particular judge to write a dissenting opinion? Despite the availability of the final judgments of the European Court of Human Rights (ECHR), none of these legal research questions can currently be answered as the ECHR’s multilingual oral hearings are not transcribed, structured, or speaker-attributed. We address this fundamental gap by presenting LaCour!, the first corpus of textual oral arguments of the ECHR, consisting of 154 full hearings (2.1 million tokens from over 267 h of video footage) in English, French, and other court languages, each linked to the corresponding final judgment documents. In addition to the transcribed and partially manually corrected text from the video, we provide sentence-level timestamps and manually annotated role and language labels. We also showcase LaCour! in a set of experiments that explore the interplay between questions and dissenting opinions. Apart from the use cases in legal NLP, we hope that law students or other interested parties will also use LaCour! as a learning resource, as it is freely available in various formats at https://huggingface.co/datasets/TrustHLT/LaCour .

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Publication Details

Journal
Artificial Intelligence and Law
Published
2024-11-28
DOI
https://doi.org/10.1007/s10506-024-09428-4
Primary Topic
Artificial Intelligence in Law
Type
article
Field-Weighted Citation Impact
0.00

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article

LaCour!: enabling research on argumentation in hearings of the European Court of Human Rights

Ivan Habernal, Lena Held
Artificial Intelligence and Law
Artificial Intelligence in Law
article

LaCour!: enabling research on argumentation in hearings of the European Court of Human Rights

Ivan Habernal, Lena Held
article en

Abstract

Abstract Why does an argument end up in the final court decision? Was it deliberated or questioned during the oral hearings? Was there something in the hearings that triggered a particular judge to write a dissenting opinion? Despite the availability of the final judgments of the European Court of Human Rights (ECHR), none of these legal research questions can currently be answered as the ECHR’s multilingual oral hearings are not transcribed, structured, or speaker-attributed. We address this fundamental gap by presenting LaCour!, the first corpus of textual oral arguments of the ECHR, consisting of 154 full hearings (2.1 million tokens from over 267 h of video footage) in English, French, and other court languages, each linked to the corresponding final judgment documents. In addition to the transcribed and partially manually corrected text from the video, we provide sentence-level timestamps and manually annotated role and language labels. We also showcase LaCour! in a set of experiments that explore the interplay between questions and dissenting opinions. Apart from the use cases in legal NLP, we hope that law students or other interested parties will also use LaCour! as a learning resource, as it is freely available in various formats at https://huggingface.co/datasets/TrustHLT/LaCour .

Artificial Intelligence and Law
Technische Universität Darmstadt (DE), Ruhr University Bochum (DE)
Deutsche Forschungsgemeinschaft, Hessisches Ministerium für Wissenschaft und Kunst, Technische Universität Darmstadt
Peace, Justice and strong institutions
Openalex Percentile: Top 66%
Artificial Intelligence in Law
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