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In this paper, we propose to study the problem of COURT VIEW GENeration fromthe fact description in a criminal case. The task aims to improve theinterpretability of charge prediction systems and help automatic legal documentgeneration. We formulate this task as a text-to-text natural languagegeneration (NLG) problem. Sequenceto-sequence model has achieved cutting-edgeperformances in many NLG tasks. However, due to the non-distinctions of factdescriptions, it is hard for Seq2Seq model to generate charge-discriminativecourt views. In this work, we explore charge labels to tackle this issue. Wepropose a label-conditioned Seq2Seq model with attention for this problem, todecode court views conditioned on encoded charge labels. Experimental resultsshow the effectiveness of our method.

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