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

Understand

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.