{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/interpretable-charge-predictions-for-criminal","title":"Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions","arxiv_id":"1802.08504","date":"2018-02-23","proceeding":"NAACL 2018 6","authors":["Hai Ye","Xin Jiang","Zhunchen Luo","WenHan Chao"],"abstract":"In this paper, we propose to study the problem of COURT VIEW GENeration from\nthe fact description in a criminal case. The task aims to improve the\ninterpretability of charge prediction systems and help automatic legal document\ngeneration. We formulate this task as a text-to-text natural language\ngeneration (NLG) problem. Sequenceto-sequence model has achieved cutting-edge\nperformances in many NLG tasks. However, due to the non-distinctions of fact\ndescriptions, it is hard for Seq2Seq model to generate charge-discriminative\ncourt views. In this work, we explore charge labels to tackle this issue. We\npropose a label-conditioned Seq2Seq model with attention for this problem, to\ndecode court views conditioned on encoded charge labels. Experimental results\nshow the effectiveness of our method.","url_abs":"http://arxiv.org/abs/1802.08504v1","url_pdf":"http://arxiv.org/pdf/1802.08504v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"interpretable-charge-predictions-for-criminal","repo_url":"https://github.com/oceanypt/Court-View-Gen","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"seq2seq","method_name":"Seq2Seq"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.08504","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}