计算机科学 ›› 2021, Vol. 48 ›› Issue (3): 227-232.doi: 10.11896/jsjkx.200700056
张春云1, 曲浩2, 崔超然1, 孙皓亮2, 尹义龙2
ZHANG Chun-yun1, QU Hao2, CUI Chao-ran1, SUN Hao-liang2, YIN Yi-long2
摘要： 法律判决预测是人工智能技术在法律领域的应用,因此对法律判决预测方法的研究对于实现智慧司法具有重要的理论价值和实际意义。传统的法律判决预测方法大都是只进行单一任务的预测或仅基于参数共享的多任务预测,并未考虑各子任务之间的序列依存关系,因此预测性能难以得到进一步的提升。文中提出了一个端到端的基于过程监督的序列多任务法律判决预测模型,在建模各子任务之间的依存关系时,通过引入过程监督来确保依赖信息的准确性,从而提升序列子任务的预测性能。将所提模型应用到CAIL2018数据集上,取得了较好的分类效果,平均分类准确率比现有的state-of-the-art方法的准确率提升了2%。
中图分类号:
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