轻量DeBERTa模型实现法律违规检测与责任人关联，性能超越大模型。
Bonafide at LegalLens 2024 Shared Task: Using Lightweight DeBERTa Based Encoder For Legal Violation Detection and Resolution
- 基于轻量DeBERTa构建实体识别与自然语言推理双系统。
- 违规检测F1达60.01%，责任关联F1达84.73%。
- 适合法律科技、合规分析领域快速部署使用。
本文提出两个系统——命名实体识别（NER）和自然语言推理（NLI），用于从非结构化文本中检测法律违规行为，并将其与可能受影响的个人进行关联。两个系统均采用轻量级DeBERTa编码器，性能优于大语言模型基线。在LegalLens挑战赛中，所提NER系统在子任务A（违规识别）上取得60.01%的F1分数；所提NLI系统在子任务B（违规与既有集体诉讼投诉匹配）上取得84.73%的F1分数。两系统分别在排行榜上位列第六和第五。论文已公开训练好的模型与推理代码。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。