面向法律文书的自然语言理解

Natural Language Understanding for Legal Text:A Review

法律人工智能因其高效、便捷的特点,近年来受到社会各界的广泛关注。法律文书是法律在社会生活中最常见的表现形式,应用自然语言理解方法智能地处理法律文书内容是一个重要的研究和应用方向。该文梳理与总结面向法律文书的自然语言理解技术,首先介绍了五类面向法律文书的自然语言理解任务形式:法律文书信息提取、类案检索、司法问答、法律文书摘要和判决预测。然后,该文探讨了运用现有自然语言理解技术应对法律文书理解的主要挑战,指出需要解决好法律文书与日常生活语言之间的表述差异性、建模好法律文书中特有的推理与论辩结构,并且需要将法条、推理模式等法律知识融入自然语言理解模型。

In recent years,legal artificial intelligence has attracted increasing attention for its efficiency and convenience.Among others,legal text is the most common manifestation in legal practice,thus,using natural language understanding method to automatically process legal text is an important direction for both academia and industry.In this paper,we provide a gentle survey to summarize recent advances on natural language understanding for legal texts.We first introduce the popular task setups,including legal information extraction,legal case retrieval,legal question answering,legal text summarization,and legal judgement prediction.We further discuss the main challenges from three perspectives:understanding the difference of languages between legal domain and open domain,understanding the rich argumentative texts in legal documents,and incorporating legal knowledge into existing natural language processing models.