Bridging the divide: technical research and application on legal judgment prediction

Legal Judgment Prediction (LJP) study is experiencing a growing need for automating legal judgment process to predict court decisions. In this context, the present paper provides a systematic literature review of previous LJP study, implementing machine learning (ML) as decision-making and natural language processing (NLP) to extract information from legal judgment documents. Relevant articles were found in reputable indexing databases through the search strategy, with the outcomes filtered by applying inclusion and exclusion criteria. Furthermore, six research questions were constructed to observe the datasets, topics/trends, NLP and ML methods, evaluation methods, and challenges. The LJP topic included three topics which were charge, law article, and term-of-penalty prediction. There were 21 NLP methods applied, emphasizing the highest implementation of Term Frequency-Inverse Document Frequency (TF-IDF) while the most implemented ML method was Support Vector Machine (SVM). Accuracy was the most used metric as an evaluation method. Additionally, this work emphasizes the importance of LJP and the potential use of NLP and ML. This study urges further investigation into NLP and ML, as well as practical uses of LJP. Low classification performance, low quantity of data, imbalanced dataset, data accessibility, data labeling, extraction of semantic information from natural language, expert involvement, generalizability issue, and multilingual datasets represent a few of the major problems that LJP faces, and the study is significant because it clarifies some of the major issues that LJP faces. Among those problems, low amounts of dataset and low classification performance were regarded as the most challenging tasks to deal with.

Legal Judgment Prediction (LJP) aims to predict the judgement results (such as legal article, charge and penalty) based on the criminal facts of the case. Most previous research in this field was based on criminal statements from court verdicts. However, each verdict actually is based on the content from indictments. For prosecutors, will the case be dismissed or processed? If the case is accepted, is the penalty a jail sentence or a fine? What is the charge and article violated? In this study, we therefore define three novel LJP tasks for prosecutors, including prosecution outcome prediction (LJP#1), imprison prediction (LJP#2) and fine prediction (LJP#3). We explore various multi-task learning (MTL) framework based on Word2Vec and BERT language model (LM) with either topology-based or message-passing mechanism. Moreover, we employed the LoRA (Low-Rank Adaptation) technique to save both computation time and resources during fine-tuning. Experimental results demonstrated that Word2Vec-based model combined with message passing architecture still has the potential to outperform large LM like BERT, while BERT-based models with a simple parallel architecture generally performed well. Finally, using LoRA for fine-tuning not only reduced training time (by 45%) but also improved performance (2.5% F1) in some LJP tasks.

Improving colloquial case legal judgment prediction via abstractive text summarization

Legal Judgment Prediction (LJP) aims to predict the judgment result based on the facts of a case and becomes a promising application of artificial intelligence techniques in the legal field. In real-world scenarios, legal judgment usually consists of multiple subtasks, such as the decisions of applicable law articles, charges, fines, and the term of penalty. Moreover, there exist topological dependencies among these subtasks. While most existing works only focus on a specific subtask of judgment prediction and ignore the dependencies among subtasks, we formalize the dependencies among subtasks as a Directed Acyclic Graph (DAG) and propose a topological multi-task learning framework, TopJudge, which incorporates multiple subtasks and DAG dependencies into judgment prediction. We conduct experiments on several real-world large-scale datasets of criminal cases in the civil law system. Experimental results show that our model achieves consistent and significant improvements over baselines on all judgment prediction tasks. The source code can be obtained from https://github.com/thunlp/TopJudge.

Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination. In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view. However, most existing works take the fact description as the sole input for LJP and ignore the external information. We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination. We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system. Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks.

The legal judgments are always based on the description of the case, the legal document. However, retrieving and understanding large numbers of relevant legal documents is a time-consuming task for legal workers. The legal judgment prediction (LJP) focus on applying artificial intelligence technology to provide decision support for legal workers. The prison term prediction(PTP) is an important task in LJP which aims to predict the term of penalty utilizing machine learning methods, thus supporting the judgement. Long-Short Term Memory(LSTM) Networks are a special type of Recurrent Neural Networks(RNN) that are capable of handling long term dependencies without being affected by an unstable gradient. Mainstream RNN models such as LSTM and GRU can capture long-distance correlation but training is time-consuming, while traditional CNN can be trained in parallel but pay more attention to local information. Both have shortcomings in case description prediction. This paper proposes a prison term prediction model for legal documents. The model adds causal expansion convolution in general TextCNN to make the model not only limited to the most important keyword segment, but also focus on the text near the key segments and the corresponding logical relationship of this paragraph, thereby improving the predicting effect and the accuracy on the data set. The causal TextCNN in this paper can understand the causal logical relationship in the text, especially the relationship between the legal text and the prison term. Since the model uses all CNN convolutions, compared with traditional sequence models such as GRU and LSTM, it can be trained in parallel to improve the training speed and can handling long term. So causal convolution can make up for the shortcomings of TextCNN and RNN models. In summary, the PTP model based on causality is a good solution to this problem. In addition, the case description is usually longer than traditional natural language sentences and the key information related to the prison term is not limited to local words. Therefore, it is crucial to capture substantially longer memory for LJP domains where a long history is required. In this paper, we propose a Causality CNN-based Prison Term Prediction model based on fact descriptions, in which the Causal TextCNN method is applied to build long effective history sizes (i.e., the ability for the networks to look very far into the past to make a prediction) using a combination of very deep networks (augmented with residual layers) and dilated convolutions. The experimental results on a public data show that the proposed model outperforms several CNN and RNN based baselines.

Legal Judgment Prediction (LJP) is a key technique for social fair. It aims to predict the judicial decisions automatically given the fact description and has great prospects in judicial assistance and management. This article focuses on the prediction of criminal judgment and proposes a legal domain-oriented method for the LJP task, by exploiting the dependencies of labels across tasks of LJP. The proposed method captures the dependencies by a prediction forward-propagate mechanism over a directed heterogeneous graph, and a novel prediction task, attribute prediction. The experiments prove the efficiency of the method and show the superior of our model on real-world datasets.

Legal judgment prediction (LJP) and decision support aim to enable machines to predict the verdict of legal cases after reading the description of facts, which is an application of artificial intelligence in the legal field. This paper proposes a legal judgment prediction model based on process supervision for the sequential dependence of each subtask in the legal judgment prediction task. Experimental results verify the effectiveness of the model framework and process monitoring mechanism adopted in this model. First, the convolutional neural network (CNN) algorithm was used to extract text features, and the principal component analysis (PCA) algorithm was used to reduce the dimension of data features. Next, the prediction model based on process supervision is proposed for the first time. When modeling the dependency relationship between sequential sub-data sets, process supervision is introduced to ensure the accuracy of the obtained dependency information, and genetic algorithm (GA) is introduced to optimize the parameters so as to improve the final prediction performance. Compared to our benchmark method, our algorithm achieved the best results on four different legal open data sets (CAIL2018_Small, CAIL2018_Large, CAIL2019_Small, and CAIL2019_Large). The realization of automatic prediction of legal judgment can not only assist judges, lawyers, and other professionals to make more efficient legal judgment but also provide legal aid for people who lack legal expertise.

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.

Legal judgment prediction(LJP) is an essential task for legal AI. While prior methods studied on this topic in a pseudo setting by employing the judge-summarized case narrative as the input to predict the judgment, neglecting critical case life-cycle information in real court setting could threaten the case logic representation quality and prediction correctness. In this paper, we introduce a novel challenging dataset from real courtrooms to predict the legal judgment in a reasonably encyclopedic manner by leveraging the genuine input of the case -- plaintiff's claims and court debate data, from which the case's facts are automatically recognized by comprehensively understanding the multi-role dialogues of the court debate, and then learnt to discriminate the claims so as to reach the final judgment through multi-task learning. An extensive set of experiments with a large civil trial data set shows that the proposed model can more accurately characterize the interactions among claims, fact and debate for legal judgment prediction, achieving significant improvements over strong state-of-the-art baselines. Moreover, the user study conducted with real judges and law school students shows the neural predictions can also be interpretable and easily observed, and thus enhancing the trial efficiency and judgment quality.

In this study, the multi-task learning(MTL) classification method based on CNN-BiGRU model is proposed, which is used to improve the accuracy and efficiently of legal judgment prediction. The subtasks of legal judgment prediction are law artivles, charges and the terms of penalty. However, the single task learning(STL) models are used to analyze legal documents, which ignoring the correlation among the subtasks. The MTL model of CNN-BiGRU enhance the task learning process, which can extract the shared information among subtasks and learn multiple tasks at the same time. Therefore, in view of the shorcomings of STL, this study explored the affilication of the MTL method to predict the three subtasks of legal judgment. CNN-BiGRU has combined the good extraction ability of CNN for local feature information and RNN for longterm dependencies information of the text classification. Compared with the CAIL2018-Small dataset, the accuracy and F1-score are the highest of all baselines models. The accuracy and F1-score of the law articles, charges and the terms of penalty are 95.1%,95.2%,72.6% and 95.2%, 95.4%, 72.7%, respectively. The proposed model improves the interpretability and the gneralization ability. The effectiveness and suitability of the model are validated on legal judgment prediction tasks.

Legal Judgment Prediction (LJP) involves examining the given input case document and recommending the judgment prediction such as applicable law sections, charges, and penalties as delivered by the judge in the court. It assists the judges and lawyers in analyzing and resolving the given case. The various steps involved in LJP equip the lawyers with supporting points to argue the case in the court and the parties involved with the probability of winning the case by predicting the judgment outcome. This paper surveys recent state-of-the-art LJP algorithms published between 2018 and 2022 by focusing on various factors such as Deep Learning (DL) and Artificial Intelligence (AI) ambient techniques, civil and criminal case types, evaluation measures, various data sets available, prediction and modelling methods, challenges, and limitations. Based on this study we derived a taxonomy that will organize the collected papers into two channels called criminal and civil cases which are further classified based on the techniques used for prediction.

Legal judgment prediction (LJP) is a fundamental task of legal artificial intelligence. It aims to automatically predict the judgment results of legal cases. Three typical subtasks are relevant law article prediction, charge prediction, and term-of-penalty prediction. Due to the wide range of potential applications, LJP has attracted a great deal of interest, prompting the development of numerous approaches. These methods mainly focus on building a more accurate representation of a case’s fact description in order to improve the performance of judgment prediction. They overlook, however, the practical judicial scenario in which human judges often compare similar law articles or possible charges before making a final decision. To this end, we propose a supervised contrastive learning framework for the LJP task. Specifically, we train the model to distinguish (1) various law articles within the same chapter of a Law and (2) similar charges of the same law article or related law articles. By this means, the fine-grained differences between similar articles/charges can be captured, which are important for making a judgment. Besides, we optimize our model by identifying cases with the same article/charge labels, allowing it to more effectively model the relationship between the case’s fact description and its associated labels. By jointly learning the LJP task with the aforementioned contrastive learning tasks, our model achieves better performance than the state-of-the-art models on two real-world datasets.

Legal judgment prediction (LJP) plays an important role in legal assistant systems and aims to provide feasible judgment suggestions, including the charges, applicable law articles, and prison term. In practice, there exist many confusing charges which result in the decline of LJP performance of the existing works. To address this issue, we introduce the legal constitutive elements as the discriminative features to distinguish confusing charges. We propose an element-driven attentive neural network model, EDA-NN, which takes the textual description of a criminal case as the input and learns both element-free and element-aware case representations. Moreover, the element-driven attention mechanism is incorporated with the hierarchical sequence encoders, to generate crucial representations oriented to the legal constitutive elements at both the word and sentence levels. With the concatenation of element-free and element-aware representations, the EDA-NN can jointly predict the legal constitutive elements and judgment results. The experiments are conducted on a real-world dataset of criminal cases in mainland China. The experimental results demonstrate that our approach significantly outperforms all the baseline models on the LJP task for criminal cases with confusing cases.

The judgments of some hot cases that exceeded public expectations seem to confirm this. Although the legal certainty is questioned, the judgment remains predictable. The predictability of judgments is a way to enhance judicial authority and maintain judicial credibility. It is also a way to achieve overlapping consensus between the judiciary and the public, provide stable value guidance and behavioral expectations for the public, and promote the generation and development of public rational trust. To enhance public legal trust by improving the predictability of judgments, it is necessary to increase the burden of reasoning for judicial judgments, avoid randomness and contingency, and ensure the adequate provision and substantial disclosure of previous judgment information, so that judgment prediction in the era of big data can truly become possible. The research is to use the optimized particle swarm algorithm as the underlying model to carry out joint modeling and prediction research on the analysis and application of problems in the prediction of legal judgments. According to experimental calculations, the optimized particle swarm algorithm can significantly improve the accuracy and universality of legal judgment prediction. After optimization, the convergence speed is increased by about 5%, and the value of the acceleration factor is more significant.