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.

翻译：本研究提出了两个系统——命名实体识别（NER）系统和自然语言推理（NLI）系统，分别用于从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可能受影响的个体进行关联。这两个系统均采用基于轻量级DeBERTa的编码器架构，其性能超越了基于大语言模型的基线方法。所提出的NER系统在LegalLens挑战赛专注于识别违规行为的子任务A中取得了60.01%的F1分数；所提出的NLI系统在LegalLens挑战赛专注于通过将违规行为与既有集体诉讼案件中的法律申诉进行匹配以解决违规关联问题的子任务B中取得了84.73%的F1分数。在LegalLens排行榜上，我们的NER系统位列第六，NLI系统位列第五。我们已公开训练好的模型及推理脚本。