Charge Prediction with Legal Attention

Bao, Qiaoben; Zan, Hongying; Gong, Peiyuan; Chen, Junyi

doi:10.1007/978-3-030-32233-5_35

Cited by 18 publications

(7 citation statements)

References 9 publications

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“…Experiments have shown that the performance and accuracy of legal prediction systems can reduce the workload of legal professionals. In work [ 23 ], the authors proposed an attentional neural network, Legalat, and used the relevant literature to improve the performance and enhance the interpretability of the charge prediction task to achieve matching the facts of the case to the relevant law, with the final verdict being rendered according to the relevant legal provisions, and finally achieving optimal performance on the actual dataset.…”

Section: Related Workmentioning

confidence: 99%

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Zheng

Liu

Sun

2022

Computational Intelligence and Neuroscience

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Legal judgment prediction is the most typical application of artificial intelligence technology, especially natural language processing methods, in the judicial field. In a practical environment, the performance of algorithms is often restricted by the computing resource conditions due to the uneven computing performance of the devices. Reducing the computational resource consumption of the model and improving the inference speed can effectively reduce the deployment difficulty of the legal judgment prediction model. To improve the prediction accuracy, enhance the model inference speed, and reduce the model memory consumption, we propose a BERT knowledge distillation-based legal decision prediction model, called KD-BERT. To reduce the resource consumption in the model inference process, we use the BERT pretraining model with lower memory requirements to be the encoder. Then, the knowledge distillation strategy transfers the knowledge to the student model of the shallow transformer structure. Experiment results show that the proposed KD-BERT has the highest F1-score compared with traditional BERT models. Its inference speed is also much faster than the other BERT models.

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Section: Related Workmentioning

confidence: 99%

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Zheng

Liu

Sun

2022

Computational Intelligence and Neuroscience

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“…(2) a multi-level matching network with parsed law-article information and we perform an extensive ablation study to validate the function of each component. (3) We show including the finegrained annotation can significantly improve the accuracy of law article recommendation and the downstream task of legal decision prediction. It is also more interpretable and humans can easily verify the results by checking the correspondence.…”

Section: Introductionmentioning

confidence: 93%

“…[48] proposed Recurrent Attention Network (RAN) to calculate the semantic mutual information between facts and articles repeatedly when making articles recommendation. [3] also jointly modeled the article recommendation task and the charge prediction task. They proposed LegalAtt which used relevant articles to filter out irrelevant information in fact based on attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…To enable this learning, we manually created a dataset on two domains: causing traffic accidents and intentionally injuring. For each domain, we downloaded judgment documents from China Judgments Online (CJO) 3 and extracted facts and law articles for annotating. We notice that in previous research, usually only charge-related law articles are extracted, while general law articles and those used for supplementary interpretation are often ignored.…”

Section: Introductionmentioning

confidence: 99%

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Learning Fine-grained Fact-Article Correspondence in Legal Cases

Ge¹,

huang²,

Shen³

et al. 2021

Preprint

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Automatically recommending relevant law articles to a given legal case has attracted much attention as it can greatly release human labor from searching over the large database of laws. However, current researches only support coarse-grained recommendation where all relevant articles are predicted as a whole without explaining which specific fact each article is relevant with. Since one case can be formed of many supporting facts, traversing over them to verify the correctness of recommendation results can be timeconsuming. We believe that learning fine-grained correspondence between each single fact and law articles is crucial for an accurate and trustworthy AI system. With this motivation, we perform a pioneering study and create a corpus with manually annotated factarticle correspondences. We treat the learning as a text matching task and propose a multi-level matching network to address it. To help the model better digest the content of law articles, we parse articles in form of premise-conclusion pairs with random forest. Experiments show that the parsed form yielded better performance and the resulting model surpassed other popular text matching baselines. Furthermore, we compare with previous researches and find that establishing the fine-grained fact-article correspondences can improve the recommendation accuracy by a large margin. Our best system reaches an F1 score of 96.3%, making it of great potential for practical use. It can also significantly boost the downstream

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“…anks to the development of machine learning and text mining techniques, more researchers formalize this task under text classification frameworks. Most of these studies attempt to extract textual features [11][12][13] or introduce some external knowledge [4,14]. However, these methods can only utilize shallow features and manually designed factors; usually the effect of these methods becomes worse when applied to other scenarios.…”

Section: Legal Judgment Prediction With the Development Ofmentioning

confidence: 99%

Legal Judgment Prediction Based on Multiclass Information Fusion

Zhu

Guo

et al. 2020

Complexity

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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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Charge Prediction with Legal Attention

Cited by 18 publications

References 9 publications

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Learning Fine-grained Fact-Article Correspondence in Legal Cases

Legal Judgment Prediction Based on Multiclass Information Fusion

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