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.
Ye et al. REF formulated the court view generation task as a text-to-text natural language generation (NLG) problem and presented a label-conditioned sequence-to-sequence model with attention to generate charge-discriminative court views.
Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions
ArXiv
ArXiv
Computer Science