@inproceedings{modi-etal-2023-semeval,
title = "{S}em{E}val-2023 Task 6: {L}egal{E}val - Understanding Legal Texts",
author = "Modi, Ashutosh and
Kalamkar, Prathamesh and
Karn, Saurabh and
Tiwari, Aman and
Joshi, Abhinav and
Tanikella, Sai Kiran and
Guha, Shouvik Kumar and
Malhan, Sachin and
Raghavan, Vivek",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.318/",
doi = "10.18653/v1/2023.semeval-1.318",
pages = "2362--2374",
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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<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.</abstract>
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%0 Conference Proceedings
%T SemEval-2023 Task 6: LegalEval - Understanding Legal Texts
%A Modi, Ashutosh
%A Kalamkar, Prathamesh
%A Karn, Saurabh
%A Tiwari, Aman
%A Joshi, Abhinav
%A Tanikella, Sai Kiran
%A Guha, Shouvik Kumar
%A Malhan, Sachin
%A Raghavan, Vivek
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F modi-etal-2023-semeval
%X 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.
%R 10.18653/v1/2023.semeval-1.318
%U https://aclanthology.org/2023.semeval-1.318/
%U https://doi.org/10.18653/v1/2023.semeval-1.318
%P 2362-2374
Markdown (Informal)
[SemEval-2023 Task 6: LegalEval - Understanding Legal Texts](https://aclanthology.org/2023.semeval-1.318/) (Modi et al., SemEval 2023)
ACL
- Ashutosh Modi, Prathamesh Kalamkar, Saurabh Karn, Aman Tiwari, Abhinav Joshi, Sai Kiran Tanikella, Shouvik Kumar Guha, Sachin Malhan, and Vivek Raghavan. 2023. SemEval-2023 Task 6: LegalEval - Understanding Legal Texts. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2362–2374, Toronto, Canada. Association for Computational Linguistics.