# An External Knowledge Enhanced Multi-label Charge Prediction Approach with Label Number Learning

arXiv: https://arxiv.org/abs/1907.02205 · HTML: https://mlsanity.com/paper/1907.02205/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges](https://mlsanity.com/paper/2204.04859/) (2022)
- [Dependency Learning for Legal Judgment Prediction with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/2112.06370/) (2021)
- [The Judge Variable: Challenging Judge-Agnostic Legal Judgment Prediction](https://mlsanity.com/paper/2507.13732/) (2025)
- [PROSLEX: A Novel Dataset for Expert-Annotated Legal Statute Prediction for Indian Judiciary](https://mlsanity.com/paper/2608.08830/) (2026)

## Abstract (arXiv)

Multi-label charge prediction is a task to predict the corresponding accusations for legal cases, and recently becomes a hot topic. However, current studies use rough methods to deal with the label number. These methods manually set parameters to select label numbers, which has an effect in final prediction quality. We propose an external knowledge enhanced multi-label charge prediction approach that has two phases. One is charge label prediction phase with external knowledge from law provisions, the other one is number learning phase with a number learning network (NLN) designed. Our approach enhanced by external knowledge can automatically adjust the threshold to get label number of law cases. It combines the output probabilities of samples and their corresponding label numbers to get final prediction results. In experiments, our approach is connected to some state of-the art deep learning models. By testing on the biggest published Chinese law dataset, we find that our approach has improvements on these models. We future conduct experiments on multi-label samples from the dataset. In items of macro-F1, the improvement of baselines with our approach is 3%-5%; In items of micro-F1, the significant improvement of our approach is 5%-15%. The experiment results show the effectiveness our approach for multi-label charge prediction.