任务(项目管理)
计算机科学
卷积神经网络
理解力
双线性插值
人工智能
答疑
考试(生物学)
自然语言处理
机器学习
程序设计语言
工程类
古生物学
系统工程
生物
计算机视觉
作者
Haiguang Zhang,Tongyue Zhang,Faxin Cao,Zhizheng Wang,Yuanyu Zhang,Yuanyuan Sun,Mark Anthony Vicente
出处
期刊:AI open
[Elsevier]
日期:2022-01-01
卷期号:3: 172-181
被引量:4
标识
DOI:10.1016/j.aiopen.2022.11.002
摘要
The National Judicial Examination of China is an essential examination for selecting legal practitioners. In recent years, people have tried to use machine learning algorithms to answer examination questions. With the proposal of JEC-QA (Zhong et al. 2020), the judicial examination becomes a particular legal task. The data of judicial examination contains two types, i.e., Knowledge-Driven questions and Case-Analysis questions. Both require complex reasoning and text comprehension, thus challenging computers to answer judicial examination questions. We propose Bilinear Convolutional Neural Networks and Attention Networks (BCA) in this paper, which is an improved version based on the model proposed by our team on the Challenge of AI in Law 2021 judicial examination task. It has two essential modules, Knowledge-Driven Module (KDM) for local features extraction and Case-Analysis Module (CAM) for the semantic difference clarification between the question stem and the options. We also add a post-processing module to correct the results in the final stage. The experimental results show that our system achieves state-of-the-art in the offline test of the judicial examination task.
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