Abstract
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.
Highlights
Previous work has brought up multiple legal assistant systems with various functions, such as finding relevant cases given the query (Chen et al, 2013), providing applicable law articles for a given case (Liu and Liao, 2005) and etc., which have substantially improved the working efficiency
We propose to study the problem of COURT VIEW GENeration from fact descriptions in cases, and we formulate it as a text-to-text natural language generation (NLG) problem (Gatt and Krahmer, 2017)
The justification for charge decision is as important as deciding the charge itself (Hendricks et al, 2016; Lei et al, 2016). (2) benefit the automatic legal document generation as legal assistant systems, by automatically generating court views from fact descriptions, to release much human labor especially for simple cases but in large amount, where fact descriptions can be obtained from legal professionals or techniques such as information extraction (Cowie and Lehnert, 1996)
Summary
Previous work has brought up multiple legal assistant systems with various functions, such as finding relevant cases given the query (Chen et al, 2013), providing applicable law articles for a given case (Liu and Liao, 2005) and etc., which have substantially improved the working efficiency. Charge prediction systems aim to determine appropriate charges such as homicide and assault for varied criminal cases by analyzing textual fact descriptions from cases (Luo et al, 2017), but ignore to give out the interpretations for the charge determination. Court view is the written explanation from judges to interprete the charge decision for certain criminal case and is the core part in a legal document, which consists of rationales and a. We only focus on generating rationales because charges can be decided by judges or charge prediction systems by analyzing the fact descriptions (Luo et al, 2017; Lin et al, 2012). COURT-VIEW-GEN has beneficial functions, in that: (1) improve the interpretability of charge prediction systems by generating rationales in court views to support the predicted charges. The justification for charge decision is as important as deciding the charge itself (Hendricks et al, 2016; Lei et al, 2016). (2) benefit the automatic legal document generation as legal assistant systems, by automatically generating court views from fact descriptions, to release much human labor especially for simple cases but in large amount, where fact descriptions can be obtained from legal professionals or techniques such as information extraction (Cowie and Lehnert, 1996)
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