Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network

Abstract

The Legal Judgment Prediction (LJP) is to determine judgment results based on the fact descriptions of the cases. LJP usually consists of multiple subtasks, such as applicable law articles prediction, charges prediction, and the term of the penalty prediction. These multiple subtasks have topological dependencies, the results of which affect and verify each other. However, existing methods use dependencies of results among multiple subtasks inefficiently. Moreover, for cases with similar descriptions but different penalties, current methods cannot predict accurately because the word collocation information is ignored. In this paper, we propose a Multi-Perspective Bi-Feedback Network with the Word Collocation Attention mechanism based on the topology structure among subtasks. Specifically, we design a multi-perspective forward prediction and backward verification framework to utilize result dependencies among multiple subtasks effectively. To distinguish cases with similar descriptions but different penalties, we integrate word collocations features of fact descriptions into the network via an attention mechanism. The experimental results show our model achieves significant improvements over baselines on all prediction tasks.

Distinguish Confusing Law Articles for Legal Judgment Prediction

“…model the explicit dependencies among subtasks with scalable directed acyclic graph forms and propose a topological multi-task learning framework for effectively solving these subtasks together. Yang et al (2019) further refine this framework by adding backward dependencies between the prediction results of subtasks. To the best of our knowledge, are the first to study the problem of discriminating confusing charges for automatically predicting applicable charges.…”

Exploring Logically Dependent Multi-task Learning with Causal Inference

“…In this paper, we consider three subtasks for Chinese LJP: Relevant Article Prediction (RAP), Charge Prediction (CP), and Prison Term Prediction (PTP). Following previous work (Zhong et al, 2018;Yang et al, 2019), we only consider those cases with single relevant article and single charge, and divide the prison term into nonoverlapping intervals. Then each subtask can be formulated as a single-label classification problem.…”

“…For LJP, we use the CAIL (Chinese AI and Law Challenge) 2018 dataset. Following (Zhong et al, 2018;Yang et al, 2019), we only consider those cases with a single law article and single charge. Meanwhile, those infrequent law articles and charges (less than 100 in the train set) are not included.…”

“…For LJP, the evaluation metrics are accuracy (Acc. ), macro P, macro R and macro F 1 scores which are consistent with (Zhong et al, 2018;Yang et al, 2019). For all models, we use the Allennlp framework to build neural networks .…”

An Element-aware Multi-representation Model for Law Article Prediction

“…Using the representation of overall law articles to extract fact information is not intuitive enough. Another one is that most of the works (Zhong et al, 2018a;Yang et al, 2019;Liu et al, 2019) only predict on single label examples. Meanwhile, in the actual judgment, many cases contain multiple relevant law articles (Zhong et al, 2018b).…”