ECHR: Legal Corpus for Argument Mining

Prakash Poudyal, Jaromir Savelka, Aagje Ieven, Marie Francine Moens, Teresa Goncalves, Paulo Quaresma

Abstract
In this paper, we publicly release an annotated corpus of 42 decisions of the European Court of Human Rights (ECHR). The corpus is annotated in terms of three types of clauses useful in argument mining: premise, conclusion, and non-argument parts of the text. Furthermore, relationships among the premises and conclusions are mapped. We present baselines for three tasks that lead from unstructured texts to structured arguments. The tasks are argument clause recognition, clause relation prediction, and premise/conclusion recognition. Despite a straightforward application of the bidirectional encoders from Transformers (BERT), we obtained very promising results F1 0.765 on argument recognition, 0.511 on relation prediction, and 0.859/0.628 on premise/conclusion recognition). The results suggest the usefulness of pre-trained language models based on deep neural network architectures in argument mining. Because of the simplicity of the baselines, there is ample space for improvement in future work based on the released corpus.
Anthology ID:
2020.argmining-1.8
Volume:
Proceedings of the 7th Workshop on Argument Mining
Month:
December
Year:
2020
Address:
Online
Editors:
Elena Cabrio, Serena Villata
Venue:
ArgMining
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
67–75
Language:
URL:
https://aclanthology.org/2020.argmining-1.8
DOI:
Bibkey:
Cite (ACL):
Prakash Poudyal, Jaromir Savelka, Aagje Ieven, Marie Francine Moens, Teresa Goncalves, and Paulo Quaresma. 2020. ECHR: Legal Corpus for Argument Mining. In Proceedings of the 7th Workshop on Argument Mining, pages 67–75, Online. Association for Computational Linguistics.
Cite (Informal):
ECHR: Legal Corpus for Argument Mining (Poudyal et al., ArgMining 2020)
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https://preview.aclanthology.org/nschneid-patch-4/2020.argmining-1.8.pdf
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