Name
Abstract | ||
|---|---|---|
Judicial Decision Prediction (JDP) aims to predict legal judgments given the fact description of a criminal case. It consists of multiple subtasks, e.g., law article prediction, charge prediction, and term of penalty prediction. Generally, a fact description contains in-depth semantic information. Besides, there exist complex dependencies among subtasks. For instance, law article prediction could guide charge prediction and term of penalty prediction. Nonetheless, the majority of previous approaches usually capture in-depth semantic information of fact description inadequately or neglect the dependencies among subtasks. In this paper, we propose a novel gated hierarchical multi-task learning network, named GHE-DAP, to jointly model multiple subtasks in JDP. Specifically, GHE-DAP combines a Gated Hierarchical Encoder (GHE) to extract in-depth semantic information of fact description from multiple perspectives, and a Dependencies Auto-learning Predictor (DAP) to learn the dependencies among subtasks dynamically. We evaluate our model on several representative subtasks, and the experimental results demonstrate that our model outperforms state-of-art baselines consistently and significantly for JDP. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2020.05.018 | Neurocomputing |
Keywords | DocType | Volume |
Judicial decision prediction,Multi-task learning,Gated hierarchical encoder,Dependencies auto-learning predictor | Journal | 411 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fanglong Yao | 1 | 0 | 0.68 |
| Xian Sun | 2 | 16 | 5.49 |
| Hongfeng Yu | 3 | 0 | 3.72 |
| Yang Yang | 4 | 435 | 30.70 |
| Wenkai Zhang | 5 | 0 | 4.73 |
| Kun Fu | 6 | 414 | 57.81 |