Abstract
Legal judgment prediction (LJP) is a crucial task in legal intelligence to predict charges, law articles and terms of penalties based on case fact description texts. Although existing methods perform well, they still have many shortcomings. First, the existing methods have significant limitations in understanding long documents, especially those based on RNNs and BERT. Secondly, the existing methods are not good at solving the problem of similar charges and do not fully and effectively integrate the information of law articles. To address the above problems, we propose a novel LJP method. Firstly, we improve the model’s comprehension of the whole document based on a graph neural network approach. Then, we design a graph attention network-based law article distinction extractor to distinguish similar law articles. Finally, we design a graph fusion method to fuse heterogeneous graphs of text and external knowledge (law article group distinction information). The experiments show that the method could effectively improve LJP performance. The experimental metrics are superior to the existing state of the art.
Highlights
Legal intelligence aims to use artificial intelligence technologies, such as natural language processing and speech recognition, to empower the field of intelligent justice
In the law article prediction task, our model improved by 0.02%, 0.03%, 0.02% and 0.04% on the CAIL-big dataset and by 0.2%, 0.09%, 0% and 0.03% on the CAIL-small dataset, respectively; in the term of penalty prediction task, our model improved by 0.02%, 0.01%, 0.03% and 0.02% on the CAIL-big dataset and by 0.23%, 0.05%, 0.13% and 0.24% on the CAIL-small dataset, respectively
The main reason is that the performance of a deep learning model depends heavily on the number of training data and the same is true for the Legal judgment prediction (LJP) task
Summary
Legal intelligence aims to use artificial intelligence technologies, such as natural language processing and speech recognition, to empower the field of intelligent justice. Legal judgment prediction (LJP) is an essential task in the field of legal intelligence, especially for countries and regions that use civil law systems in which the judge decides the charge and the term of penalty based on the fact description of the case and the relevant law articles. The purpose of the LJP task is to predict the judgment of a case (charge, law article and term of penalty) based on the case fact description, which can help legal practitioners to judge the case and can give professional legal advice to people who are not in the legal field. LJP tasks are generally regarded as text classification tasks [1].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
