Distinguish Confusing Law Articles for Legal Judgment Prediction
Table 4: Ablation analysis on CAIL-small.
['Tasks Metrics', 'Law Acc.', 'Law F1', 'Charge Acc.', 'Charge F1', 'Penalty Acc.', 'Penalty F1']
To further illustrate the significance of considering the difference between law articles, we conducted ablation experiments on model LADAN+MTL with dataset CAIL-small. To prove the effectiveness of our graph construction layer (GCL), we build a LADAN model with the GCL’s removing threshold τ=0 To evaluate the importance of considering the difference among law articles, we remove both GCL and GDO from LADAN by setting τ=1.0 (i.e., “-no GCL is more critical than GDO because GDO has a limited performance when the law article communities obtained by GCL are not accurate. When removing both GCL and GDO, the accuracy of LADAN decreases to that of HARNN+MTL, which powerfully demonstrates the effectiveness of our method exploiting differences among similar law articles.