# Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction

arXiv: https://arxiv.org/abs/1911.05202 · HTML: https://mlsanity.com/paper/1911.05202/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2019.

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

- [JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System](https://mlsanity.com/paper/2503.14258/) (2025)
- [Enhancing Judgment Document Generation via Agentic Legal Information Collection and Rubric-Guided Optimization](https://mlsanity.com/paper/2605.02011/) (2026)
- [From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction](https://mlsanity.com/paper/2403.04369/) (2024)
- [AppellateGen: A Benchmark for Appellate Legal Judgment Generation](https://mlsanity.com/paper/2601.01331/) (2026)

## Abstract (arXiv)

Charge prediction, determining charges for criminal cases by analyzing the textual fact descriptions, is a promising technology in legal assistant systems. In practice, the fact descriptions could exhibit a significant intra-class variation due to factors like non-normative use of language, which makes the prediction task very challenging, especially for charge classes with too few samples to cover the expression variation. In this work, we explore to use the charge definitions from criminal law to alleviate this issue. The key idea is that the expressions in a fact description should have corresponding formal terms in charge definitions, and those terms are shared across classes and could account for the diversity in the fact descriptions. Thus, we propose to create auxiliary fact representations from charge definitions to augment fact descriptions representation. The generated auxiliary representations are created through the interaction of fact description with the relevant charge definitions and terms in those definitions by integrated sentence- and word-level attention scheme. Experimental results on two datasets show that our model achieves significant improvement than baselines, especially for classes with few samples.