Learning to Predict Charges for Criminal Cases with Legal Basis
Table 2: Refined Article Extraction Performance
['[ITALIC] β', '[BOLD] Prec@1', '[BOLD] MAP', '[BOLD] Charge_F1']
Prec@1 refers to top 1 precision, and MAP refers to mean average precision. We can see that, even if there is no supervision over the article attention (β=0), our model still has reasonable performance on re-ranking the k articles. When the attention supervision is employed, the extraction quality improves significantly, and keeps increasing as β goes up. However, the charge prediction performance does not always increase with the article extraction quality, and the best performance is achieved when β=0.1. This is not surprising, since there exists a tradeoff between the benefits of more accurate article extraction and the less model capacity left for charge classification due to the increased emphasis on the article extraction performance. The promising article extraction results also confirm the ability of our model to provide legal basis for the charge prediction.