Laws and their interpretations, legal arguments and agreements\ are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.
翻译：法律及其解释、法律论据和协议通常以书面形式表述,导致产生大量法律文本,其分析是法律实践的核心,随着这些文献收集规模的扩大,其分析变得越来越精细。自然语言理解技术可以成为支持法律从业人员开展这些工作的宝贵工具。然而,这些技术的有用性在很大程度上取决于目前最先进的模式能否在法律领域将各种任务加以概括。为了回答这一目前尚未解决的问题,我们引入了法律通用语言理解评价基准(LexGLUE),这是一套数据集,用于以标准化的方式评估一整套法律上的国家语言理解评价任务中的示范性业绩。我们还对一些通用的、面向法律的模式进行了评价和分析,表明后者在多项任务中不断提供业绩改进。
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