# Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions

arXiv: https://arxiv.org/abs/1802.08504 · HTML: https://mlsanity.com/paper/1802.08504/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2018.

## Built on by (most cited first, 30 of 36)

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- [From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction](https://mlsanity.com/paper/2403.04369/) (2024)
- [DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction](https://mlsanity.com/paper/2211.15424/) (2022)
- [SEMDR: A Semantic-Aware Dual Encoder Model for Legal Judgment Prediction with Legal Clue Tracing](https://mlsanity.com/paper/2408.09717/) (2024)
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- [A Hierarchical Neural Framework for Classification and its Explanation in Large Unstructured Legal Documents](https://mlsanity.com/paper/2309.10563/) (2023)
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## Abstract (arXiv)

In this paper, we propose to study the problem of COURT VIEW GENeration from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequenceto-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.