杨亮,周逢清,张琍,毛国庆,易斌,林鸿飞. 基于生成对抗网络的控辩焦点识别[J]. 中文信息学报, 2020, 34(9): 89-96.
YANG Liang, ZHOU Fengqing, ZHANG Li, MAO Guoqing, YI Bin, LIN Hongfei. Argument Recognition Based on Generative Adversarial Networks. , 2020, 34(9): 89-96.
Argument Recognition Based on Generative Adversarial Networks
YANG Liang1, ZHOU Fengqing1, ZHANG Li2, MAO Guoqing3, YI Bin1, LIN Hongfei1
1.College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China; 2.Beijing Institute of Computer Technology and Application, Beijing 100854, China; 3.Beijing GridSum Technology Co., Ltd., Beijing 100083, China
Abstract：In the process of trial in the field of justice, the prosecution and the defense often hold different views around the argument of the case, which is also the key factors to the final judgment of the case. To identify the arguments in the cases, this paper introduce the text summarization model since the composition of the argument mostly depends on the analysis and summary of the case text. We construct the generation model of the argument by combining the generative adversarial network, and then obtain the argument of the case. Experimented on the real judicial data obtained from the website of China Judgements Online, the results show that the proposed model improves the accuracy in the task of argument recognition. This method can be applied as an auxiliary role in the pre-court preplan and trial of the case for procuratorial personnel in real application.
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2020, Vol. 34 
