Name
Title | ||
|---|---|---|
Commonalities-, specificities-, and dependencies-enhanced multi-task learning network for judicial decision prediction |
Abstract | ||
|---|---|---|
Judicial Decision Prediction (JDP) aims to determine judicial decisions per the fact description of a criminal case. It comprises multiple subtasks, i.e., law article prediction, charge prediction, and term of the penalty prediction. Besides, there exist three properties among the subtasks, i.e., Commonalities,Specificities, and Dependencies. Nonetheless, existing approaches are usually well-designed for only a specific subtask, or take one of the properties into consideration for multiple subtasks. In this paper, we propose a novel Commonalities-, Specificities- and Dependencies-Enhanced Multi-Task Learning Network, to unify multiple subtasks accompanied by the properties in a framework. Further, while handling the Dependencies, we elaborate a learning module to ensure each subtask to learn contributions from other subtasks to varying degrees, a denoising module to minimize noise interferences among subtasks, and a reinforcing module to guarantee further enhancement for each subtask. Experimental results on two widely used datasets demonstrate that our model significantly and consistently outperforms previous state-of-the-art methods on most evaluation metrics across all subtasks. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2020.10.010 | Neurocomputing |
Keywords | DocType | Volume |
Judicial decision prediction,Multi-task learning,Commonalities-specificities encoder,Dependencies decoder | Journal | 433 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fanglong Yao | 1 | 0 | 0.68 |
| Xian Sun | 2 | 16 | 5.49 |
| Hongfeng Yu | 3 | 0 | 3.72 |
| Wenkai Zhang | 4 | 0 | 4.73 |
| Kun Fu | 5 | 414 | 57.81 |