SemEval-2023 Task 6: LegalEval - Understanding Legal Texts

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Abstract

In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction. In total 26 teams (approx. 100 participants spread across the world) submitted systems paper. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for improvement. This paper describes the tasks, and analyzes techniques proposed by various teams.

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APA

Modi, A., Kalamkar, P., Karn, S., Tiwari, A., Joshi, A., Tanikella, S. K., … Raghavan, V. (2023). SemEval-2023 Task 6: LegalEval - Understanding Legal Texts. In 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop (pp. 2362–2374). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.semeval-1.318

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