Abstract
In this work, we present two systems -- Named Entity Resolution (NER) and Natural Language Inference (NLI) -- for detecting legal violations within unstructured textual data and for associating these violations with potentially affected individuals, respectively. Both these systems are lightweight DeBERTa based encoders that outperform the LLM baselines. The proposed NER system achieved an F1 score of 60.01\% on Subtask A of the LegalLens challenge, which focuses on identifying violations. The proposed NLI system achieved an F1 score of 84.73\% on Subtask B of the LegalLens challenge, which focuses on resolving these violations by matching them with pre-existing legal complaints of class action cases. Our NER system ranked sixth and NLI system ranked fifth on the LegalLens leaderboard. We release the trained models and inference scripts.
Abstract (translated)
在这项工作中，我们提出了两个系统——命名实体解析（NER）和自然语言推理（NLI），分别用于在非结构化文本数据中检测法律违规行为，并将这些违规行为与潜在受影响的个人关联起来。这两个系统都是基于轻量级DeBERTa编码器，其性能优于LLM基线模型。所提出的NER系统在LegalLens挑战赛的子任务A上实现了60.01%的F1分数，该子任务专注于识别违规行为。所提出的NLI系统在LegalLens挑战赛的子任务B上实现了84.73%的F1分数，该子任务侧重于通过将违规行为与现有集体诉讼案件中的法律投诉相匹配来解决这些违规问题。我们的NER系统在LegalLens排行榜上排名第六，而NLI系统则排名第五。我们发布了训练好的模型和推理脚本。
URL
https://arxiv.org/abs/2410.22977
