Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network
This work addresses a domain-specific problem in legal AI by improving prediction accuracy for legal judgments, though it appears incremental as it builds on existing methods with novel mechanisms.
The paper tackled the problem of Legal Judgment Prediction (LJP) by addressing inefficient use of dependencies among subtasks and inaccurate predictions for cases with similar descriptions but different penalties, resulting in significant improvements over baselines on all prediction tasks.
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.