How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

By Haoxiang Zhong, Chaojun Xiao, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, Maosong Sun. 2020

435 citations recorded by the source corpus.

Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. In recent years, LegalAI has drawn increasing attention rapidly from both AI researchers and legal professionals, as LegalAI is beneficial to the legal system for liberating legal professionals from a maze of paperwork. Legal professionals often think about how to solve tasks from rule-based and symbol-based methods, while NLP researchers concentrate more on data-driven and embedding methods. In this paper, we introduce the history, the current state, and the future directions of research in LegalAI. We illustrate the tasks from the perspectives of legal professionals and NLP researchers and show several representative applications in LegalAI. We conduct experiments and provide an in-depth analysis of the advantages and disadvantages of existing works to explore possible future directions. You can find the implementation of our work from https://github.com/thunlp/CLAIM.

Subfields: Artificial Intelligence, Political Science and International Relations

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Citations in the corpus

Undesirable biases in NLP: Averting a crisis of measurement (2022, background): “…transcribed and analyzed to improve service (e.g., Verma et al., 2011), millions of medical and legal texts are automatically searched to find relevant passages, at times supporting decisions that may literally be matters of life and death (e.g., Wang et al., 2018; Zhong et al., 2020).”
Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases (2021, background): “They can help judges identify more quickly facts that support the alleged violations, contributing towards more informed judicial decision making (Zhong et al., 2020).”
Policy Compliance Detection via Expression Tree Inference (2022, background): “Reasoning over policies expressed in natural language is an important task in machine comprehension (Zhong et al., 2020).”
Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents (2021, positive): “It is an established fact that enhancing the models with legal knowledge is quite necessary for many LegalAI tasks (Zhong et al., 2020a).”
When does pretraining help?: assessing self-supervised learning for law and the CaseHOLD dataset of 53,000+ legal holdings (2021, methodology): “This is explained in part due to the expense of labeling decisions and challenges around compiling large sets of legal documents [32], leading approaches above to rely on non-English datasets [49, 50] or proprietary datasets [14].”