Learning Fine-Grained Fact-Article Correspondence in Legal Cases

@article{Ge2021LearningFF,
  title={Learning Fine-Grained Fact-Article Correspondence in Legal Cases},
  author={Jidong Ge and Yunyun Huang and Xiaoyu Shen and Chuanyi Li and Wei Hu and Bin Luo},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  year={2021},
  volume={29},
  pages={3694-3706}
}
  • Jidong Ge, Yunyun Huang, B. Luo
  • Published 21 April 2021
  • Computer Science
  • IEEE/ACM Transactions on Audio, Speech, and Language Processing
Automatically recommending relevant law articles to a given legal case has attracted much attention as it can greatly release human labor from searching over the large database of laws. However, current researches only support coarse-grained recommendation where all relevant articles are predicted as a whole without explaining which specific fact each article is relevant with. Since one case can be formed of many supporting facts, traversing over them to verify the correctness of recommendation… 

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