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

Hai Ye, Xin Jiang, Zhunchen Luo, Wenhan Chao


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. Sequence-to-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.
Anthology ID:
N18-1168
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1854–1864
Language:
URL:
https://www.aclweb.org/anthology/N18-1168
DOI:
10.18653/v1/N18-1168
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1168.pdf
Video:
 http://vimeo.com/277673836