论文标题
民事诉讼中的法律论证推理任务
The Legal Argument Reasoning Task in Civil Procedure
论文作者
论文摘要
我们从美国民事诉讼程序的领域提出了一项新的NLP任务和数据集。数据集的每个实例都包含对案例的一般介绍，一个特定的问题以及可能的解决方案参数，并伴随着有关该参数在这种情况下为什么适用的详细分析。由于数据集是基于针对法律学生的书，因此我们认为它代表了基准对现代法律语言模型进行基准测试的真正复杂任务。我们的基线评估表明，对法律变压器进行微调比随机基线模型提供了一些优势，但是我们的分析表明，推断法律论证的实际能力仍然是一个具有挑战性的开放研究问题。
We present a new NLP task and dataset from the domain of the U.S. civil procedure. Each instance of the dataset consists of a general introduction to the case, a particular question, and a possible solution argument, accompanied by a detailed analysis of why the argument applies in that case. Since the dataset is based on a book aimed at law students, we believe that it represents a truly complex task for benchmarking modern legal language models. Our baseline evaluation shows that fine-tuning a legal transformer provides some advantage over random baseline models, but our analysis reveals that the actual ability to infer legal arguments remains a challenging open research question.
