Modeling legal reasoning and argumentation justifying decisions in cases has always been central to AI & Law, yet contemporary developments in legal NLP have increasingly focused on statistically classifying legal conclusions from text. While conceptually simpler, these approaches often fall short in providing usable justifications connecting to appropriate legal concepts. This paper reviews both traditional symbolic works in AI & Law and recent advances in legal NLP, and distills possibilities of integrating expert-informed knowledge to strike a balance between scalability and explanation in symbolic vs. data-driven approaches. We identify open challenges and discuss the potential of modern NLP models and methods that integrate

翻译：建模法律推理与论证以证明案件决策的合理性一直是人工智能与法律领域的核心议题，然而当代法律自然语言处理的发展日益聚焦于从文本中统计分类法律结论。尽管概念上更为简单，这些方法往往难以提供与适当法律概念相连接的可解释论证。本文回顾了人工智能与法律领域的传统符号主义研究以及法律自然语言处理的最新进展，并提炼出整合专家知识的可能性，以在符号主义与数据驱动方法的可扩展性与可解释性之间取得平衡。我们指出了尚未解决的挑战，并探讨了整合专家知识的现代自然语言处理模型与方法的潜力。