Gotowe bibliografie tematyczne / Legal Judgment Prediction

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Gotowa bibliografia na temat „Legal Judgment Prediction”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Legal Judgment Prediction"

Zhong, Haoxi, Yuzhong Wang, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, and Maosong Sun. "Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1250–57. http://dx.doi.org/10.1609/aaai.v34i01.5479.

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Legal Judgment Prediction (LJP) aims to predict judgment results according to the facts of cases. In recent years, LJP has drawn increasing attention rapidly from both academia and the legal industry, as it can provide references for legal practitioners and is expected to promote judicial justice. However, the research to date usually suffers from the lack of interpretability, which may lead to ethical issues like inconsistent judgments or gender bias. In this paper, we present QAjudge, a model based on reinforcement learning to visualize the prediction process and give interpretable judgments. QAjudge follows two essential principles in legal systems across the world: Presumption of Innocence and Elemental Trial. During inference, a Question Net will select questions from the given set and an Answer Net will answer the question according to the fact description. Finally, a Predict Net will produce judgment results based on the answers. Reward functions are designed to minimize the number of questions asked. We conduct extensive experiments on several real-world datasets. Experimental results show that QAjudge can provide interpretable judgments while maintaining comparable performance with other state-of-the-art LJP models. The codes can be found from https://github.com/thunlp/QAjudge.

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Chen, Junyi, Lan Du, Ming Liu, and Xiabing Zhou. "Mulan: A Multiple Residual Article-Wise Attention Network for Legal Judgment Prediction." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 4 (2022): 1–15. http://dx.doi.org/10.1145/3503157.

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Legal judgment prediction (LJP) is used to predict judgment results based on the description of individual legal cases. In order to be more suitable for actual application scenarios in which the case has cited multiple articles and has multiple charges, we formulate legal judgment prediction as a multiple label learning problem and present a deep learning model that can effectively encode the content of each legal case via a multi-residual convolution neural network and the semantics of law articles via an article encoder. An article-wise attention mechanism is proposed to fuse the two types of encoded information. Experimental results derived on the CAIL2018 datasets show that our model provides a significant performance improvement over the existing neural models in predicting relevant law articles and charges.

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Shang, Xuerui. "A Computational Intelligence Model for Legal Prediction and Decision Support." Computational Intelligence and Neuroscience 2022 (June 24, 2022): 1–8. http://dx.doi.org/10.1155/2022/5795189.

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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.

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Zhu, Kongfan, Rundong Guo, Weifeng Hu, Zeqiang Li, and Yujun Li. "Legal Judgment Prediction Based on Multiclass Information Fusion." Complexity 2020 (October 26, 2020): 1–12. http://dx.doi.org/10.1155/2020/3089189.

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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.

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Gan, Leilei, Kun Kuang, Yi Yang, and Fei Wu. "Judgment Prediction via Injecting Legal Knowledge into Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12866–74. http://dx.doi.org/10.1609/aaai.v35i14.17522.

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Legal Judgment Prediction (LJP) is a key problem in legal artificial intelligence, which is aimed to predict a law case's judgment based on a given text describing the facts of the law case. Most of the previous work treats LJP as a text classification task and generally adopts deep neural networks (DNNs) based methods to solve it. However, existing DNNs based work is data-hungry and hard to explain which legal knowledge is based on to make such a prediction. Thus, injecting legal knowledge into neural networks to interpret the model and improve performance remains a significant problem. In this paper, we propose to represent declarative legal knowledge as a set of first-order logic rules and integrate these logic rules into a co-attention network-based model explicitly. The use of logic rules enhances neural networks with explicit logical reason capabilities and makes the model more interpretable. We take the civil loan scenario as a case study and demonstrate the effectiveness of the proposed method through comprehensive experiments and analysis conducted on the collected dataset.

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Lyu, Yougang, Zihan Wang, Zhaochun Ren, et al. "Improving legal judgment prediction through reinforced criminal element extraction." Information Processing & Management 59, no. 1 (2022): 102780. http://dx.doi.org/10.1016/j.ipm.2021.102780.

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Ma, Wenqing. "Artificial Intelligence-Assisted Decision-Making Method for Legal Judgment Based on Deep Neural Network." Mobile Information Systems 2022 (October 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/4636485.

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With the arrival of the third revolution of artificial intelligence, the applications of artificial intelligence in the fields of automatic driving, image recognition, smart home, machine translation, medical services, e-sports, and so on can be seen everywhere, and topics about artificial intelligence are constantly emerging. Since 2017, the discussion on artificial intelligence in the field of law has become more and more active. In this context, the application of artificial intelligence in the field of legal judgment and the hypothetical system based on this technology in court judgment has also become the object of discussion from time to time. In this paper, based on the artificial intelligence decision-making method of the deep neural network, aiming at the three subtasks of legal judgment prediction, namely, crime prediction, law recommendation, and sentence prediction, a multi-task judgment prediction model BERT12multi and a sentence interval prediction model BERT-Text CNN are proposed, which improve the prediction accuracy and adopt the knowledge distillation strategy to compress the model parameters and improve the reasoning speed of the judgment model. Experiments on the CAIL2018 data set show that the performance of the deep neural network model in crime prediction and law recommendation tasks can be significantly improved by adopting the pre training model adaptive training, grouping focus loss, and gradient confrontation training strategies. Using a step-by-step sentence prediction strategy can realize the weight sharing of pre training model and make use of the prediction results of charges and laws in sentence prediction. The recall training-prediction strategy can avoid error accumulation and improve the accuracy of sentence prediction. By integrating the artificial intelligence decision-making method, the case reasoning speed can be greatly improved, the highest compressible model volume can be about 11% of the original one, and the reasoning speed can be increased by about 8 times. At the same time, performance close to that of the deep neural model can be obtained, which is superior to other legal decision prediction models based on word embedding.

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Zhang, Hu, Bangze Pan, and Ru Li. "Legal Judgment Elements Extraction Approach with Law Article-aware Mechanism." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 3 (2022): 1–15. http://dx.doi.org/10.1145/3485244.

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Legal judgment elements extraction (LJEE) aims to identify the different judgment features from the fact description in legal documents automatically, which helps to improve the accuracy and interpretability of the judgment results. In real court rulings, judges usually need to scan both the fact descriptions and the law articles repeatedly to find out the relevant information, and it is hard to acquire the key judgment features quickly, so legal judgment elements extraction is a crucial and challenging task for legal judgment prediction. However, most existing methods follow the text classification framework, which fails to model the attentive relations of the law articles and the legal judgment elements. To address this issue, we simulate the working process of human judges, and propose a legal judgment elements extraction method with a law article-aware mechanism, which captures the complex semantic correlations of the law article and the legal judgment elements. Experimental results show that our proposed method achieves significant improvements than other state-of-the-art baselines on the element recognition task dataset. Compared with the BERT-CNN model, the proposed “All labels Law Articles Embedding Model (ALEM)” improves the accuracy, recall, and F1 value by 0.5, 1.4 and 1.0, respectively.

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Li, Shang, Hongli Zhang, Lin Ye, Xiaoding Guo, and Binxing Fang. "MANN: A Multichannel Attentive Neural Network for Legal Judgment Prediction." IEEE Access 7 (2019): 151144–55. http://dx.doi.org/10.1109/access.2019.2945771.

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He, Congqing, Tien-Ping Tan, Xiaobo Zhang, and Sheng Xue. "Knowledge-Enriched Multi-Cross Attention Network for Legal Judgment Prediction." IEEE Access 11 (2023): 87571–82. http://dx.doi.org/10.1109/access.2023.3305259.

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