# Accusations and Law Articles Prediction in the Field of Environmental Protection
## 6 Discussion
### Comparison with Existing Models  
Our proposed EKICAN model demonstrates superior performance compared to several state-of-the-art legal judgment prediction models. As shown in Table 4, EKICAN achieves a Macro-F1 score of 91.9% for charge prediction and 74.3% for article prediction, outperforming models such as NeurJudge (90.1% and 72.1%) and TopJudge (88.4% and 72.7%). While the performance difference between EKICAN and NeurJudge is relatively small, the improvement is still significant, particularly when considering the complexity of the task. EKICAN's innovative integration of external legal knowledge and its cross-attention mechanism enable it to better capture subtle differences between semantically similar charges, which is crucial in the context of environmental law where violations are often difficult to differentiate based on legal language alone. Additionally, EKICAN's ability to handle data imbalance through the use of large pre-trained models for text generation further enhances its predictive accuracy and robustness. Unlike traditional models that rely solely on labeled data, EKICAN leverages synthetic data generation to balance the dataset, reducing bias and improving generalization across different case types. The ability of EKICAN to differentiate between charges in environmental law is of paramount importance, given the high similarity between many types of legal violations in this domain. NeurJudge, while effective, is a more general-purpose model and lacks the domain-specific focus and integration of external knowledge that EKICAN provides. This allows EKICAN to perform better in specialized tasks, such as those involving the interpretation of complex environmental statutes. TopJudge, on the other hand, employs a hierarchical attention network architecture that allows it to capture contextual dependencies and relationships between different parts of the case. However, despite its strong architecture, it does not integrate external knowledge, particularly domain-specific legal knowledge, into its prediction process. This limits its ability to handle cases that require an understanding of complex legal statutes, such as those in environmental law. The absence of external knowledge infusion in TopJudge means it struggles to differentiate between semantically similar environmental law violations, whereas EKICAN's incorporation of external legal knowledge, combined with its cross-attention mechanism, enables it to more effectively capture the unique nuances of environmental law, leading to superior performance. Moreover, while models like RoBERTa and Transformer have demonstrated strong performance in general legal prediction tasks, EKICAN's domain-specific focus on environmental law allows it to better capture the nuances of this field. Environmental law is often characterized by a high degree of semantic overlap and similar legal logic, which can be challenging for general-purpose models. EKICAN addresses this by incorporating specialized environmental law knowledge and leveraging external datasets tailored for environmental statutes and case law. This research focuses on the application of deep learning models to predict judgments in the environmental law sector, providing a detailed comparison of various neural network architectures. Among these, the EKICAN model demonstrated exceptional capabilities, significantly outperforming traditional and contemporary models by effectively integrating external environmental law knowledge. Our results underscore the importance of tailored approaches in environmental law judgment prediction. The EKICAN model's superior performance is attributed to its sophisticated attention mechanisms and the incorporation of external knowledge, which are pivotal in comprehending the complex terminologies and statutes specific to environmental law. The inclusion of external datasets specifically curated for environmental statutes and cases proved to enhance the accuracy and contextual understanding of the models, suggesting a promising direction for future research.