Learning to Predict Charges for Judgment with Legal Graph

Chen, Si; Wang, Pengfei; Fang, Wei; Deng, Xingchen; Feng, Zhiyong

doi:10.1007/978-3-030-30490-4_20

Cited by 15 publications

(6 citation statements)

References 12 publications

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“…erefore, researchers take advantage of other technologies to improve the interpretability and generalization of the model. For example, Jiang et al utilized the deep reinforcement learning to derive short snippets of documents from the fact descriptions to predict charges [15], and Chen et al proposed a Legal Graph Network (LGN) to achieve high-precision classification of crimes [16]. Due to the rareness of some types of cases in real life, the few-shot problem is inevitable.…”

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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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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“…To improve prediction accuracy, [20] focused on word collocations information and integrated word collocations features of fact descriptions into the network via an attention mechanism. These works don't consider the charge information, which is proved to be useful in predicting charges [4]. Fortunately, Hu et al [6] constructed several discriminative attributes of charges as the internal mapping between fact descriptions and charges, which offer effective signals for distinguishing confusing charges.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-aware Method for Confusing Charge Prediction

Cheng

et al. 2020

Preprint

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Automatic charge prediction task aims to determine the final charges based on fact descriptions of criminal cases, which is a vital application of legal assistant systems. Conventional works usually depend on fact descriptions to predict charges while ignoring the legal schematic knowledge, which makes it difficult to distinguish confusing charges. In this paper, we propose a knowledge-attentive neural network model, which introduces legal schematic knowledge about charges and exploit the knowledge hierarchical representation as the discriminative features to differentiate confusing charges. Our model takes the textual fact description as the input and learns fact representation through a graph convolutional network. A legal schematic knowledge transformer is utilized to generate crucial knowledge representations oriented to the legal schematic knowledge at both the schema and charge levels. We apply a knowledge matching network for effectively incorporating charge information into the fact to learn knowledge-aware fact representation. Finally, we use the knowledge-aware fact representation for charge prediction. We create two real-world datasets and experimental results show that our proposed model can outperform other state-of-the-art baselines on accuracy and F1 score, especially on dealing with confusing charges.

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“…Jiang et al, used deep reinforcement learning to obtain simple document features from factual descriptions to predict crimes [5]. Chen et al, proposed a legal graph network (LGN) to achieve high-accuracy crime prediction [6].…”

Section: Introductionmentioning

confidence: 99%

Research on a Decision Prediction Method Based on Causal Inference and a Multi-Expert FTOPJUDGE Mechanism

et al. 2022

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Legal judgement prediction (LJP) is a crucial part of legal AI, and its goal is to predict the outcome of a case based on the information in the description of criminal facts. This paper proposes a decision prediction method based on causal inference and a multi-expert FTOPJUDGE mechanism. First, a causal inference algorithm was adopted to process unstructured text. This process did not require very much manual intervention to better mine the information in the text. Then, a neural network dedicated to each task was set up, and a neural network that simultaneously served multiple tasks was also set up. Finally, the pre-trained language model Lawformer was used to provide knowledge for downstream tasks. By using the public data set CAIL2018 and comparing it with current mainstream decision prediction models, it was shown that the model significantly improved the performance of downstream tasks and achieved great improvements in multiple indicators. Through ablation experiments, the effectiveness and rationality of each module of the proposed model were verified. The method proposed in this study achieved reasonably good performance in legal judgment prediction, which provides a promising solution for legal judgment prediction.

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Learning to Predict Charges for Judgment with Legal Graph

Cited by 15 publications

References 12 publications

Legal Judgment Prediction Based on Multiclass Information Fusion

Legal Judgment Prediction Based on Multiclass Information Fusion

Knowledge-aware Method for Confusing Charge Prediction

Research on a Decision Prediction Method Based on Causal Inference and a Multi-Expert FTOPJUDGE Mechanism

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