How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020)

Abstract: 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 .

## introduction legal artificial intelligence (legalai) mainly focuses on applying artificial intelligence technology to help legal tasks. the majority of the resources in this field are presented in text forms, such as judgment documents, contracts, and legal opinions. therefore, most legalai tasks are based on natural language processing (nlp) technologies. legalai plays a significant role in the legal domain, as they can reduce heavy and redundant work for legal professionals. many tasks in the legal domain require the expertise of legal practitioners and a thorough understanding of various legal documents. retrieving and understanding legal documents take lots of time, even for legal professionals. ## embedding-based methods first, we describe embedding-based methods in legalai, also named as representation learning. embedding-based methods emphasize on representing legal facts and knowledge in embedding space, and they can utilize deep learning methods for corresponding tasks. ## symbol-based methods in this section, we describe symbol-based methods, also named as structured prediction methods. symbol-based methods are involved in utilizing legal domain symbols and knowledge for the tasks of legalai. the symbolic legal knowledge, such as events and relationships, can provide interpretability. deep learning methods can be employed for symbol-based methods for better performance. ## applications of legalai in this section, we will describe several typical applications in legalai, including legal judgment prediction, similar case matching and legal question answering. legal judgment prediction and similar case matching can be regarded as the core function of judgment in civil law and common law system, while legal question answering can provide consultancy for those who are unfamiliar with the legal domain. therefore, exploring these three tasks can cover most aspects of legalai. ## conclusion in this paper, we describe the development status of various legalai tasks and discuss what we can do in the future. in addition to these applications and tasks we have mentioned, there are many other tasks in legalai like legal text summarization and information extraction from legal contracts. nevertheless, no matter what kind application is, we can apply embedding-based methods for better performance, together with symbol-based methods for more interpretability. besides, the three main challenges of legal tasks remain to be solved. knowledge modelling, legal reasoning, and interpretability are the foundations on which legalai can reliably serve the legal domain. some existing methods are trying to solve these problems, but there is still a long way for researchers to go. in the future, for these existing tasks, researchers can focus on solving the three most pressing challenges of legalai combining embedding-based and symbol-based methods. for tasks that do not yet have a dataset or the datasets are not large enough, we can try to build a large-scale and highquality dataset or use few-shot or zero-shot methods to solve these problems. furthermore, we need to take the ethical issues of legalai seriously. applying the technology of legalai directly to the legal system will bring ethical issues like gender bias and racial discrimination. the results given by these methods cannot convince people. to address this issue, we must note that the goal of legalai is not replacing the legal professionals but helping their work. as a result, we should regard the results of the models only as a reference. otherwise, the legal system will no longer be reliable. for example, professionals can spend more time on complex cases and leave the simple cases for the model. however, for safety, these simple cases must still be reviewed. in general, legalai should play as a supporting role to help the legal system.