SemEval-2023 Task 6: LegalEval - Understanding Legal Texts

被引:0
|
作者
Modi, Ashutosh [1 ]
Kalamkar, Prathamesh [2 ]
Karn, Saurabh [3 ]
Tiwari, Aman [2 ]
Joshi, Abhinav [1 ]
Tanikella, Sai Kiran [1 ]
Guha, Shouvik Kumar [5 ]
Malhan, Sachin [3 ]
Raghavan, Vivek [4 ]
机构
[1] Indian Inst Technol Kanpur, Kanpur, Uttar Pradesh, India
[2] Thoughtworks, Bengaluru, India
[3] Agami, Bengaluru, India
[4] EkStep, Bengaluru, India
[5] NUJS, Kolkata, W Bengal, India
关键词
COURT JUDGMENT PREDICTION; RHETORICAL ROLES; NLP; IDENTIFICATION; REPRESENTATION;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:2362 / 2374
页数:13
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