## 7 Results and Performance Analysis
### Multilingual Transfer and Zero-Shot Generalization  
Figure 8 presents the results obtained for the TriagE-NLU performance evaluation regarding semantic parsing and intervention prediction by language. The results illustrate the F1 scores of TriagE-NLU across five languages, English, Spanish, Romanian, Arabic, and Mandarin, for two key tasks: semantic parsing and intervention prediction. * English achieves the highest performance, with 0.92 F1 for semantic parsing and 0.89 for intervention prediction, indicating strong performance in high-resource language settings. * Spanish follows closely with 0.90 parsing F1 and 0.88 intervention F1, maintaining robust performance. * Romanian and Arabic, both lower-resource languages, show a modest drop: 0.89 and 0.88% parsing F1, and 0.87 and 0.86 intervention F1, respectively. Figure 8: TriagE-NLU evaluation: performance by language. * Mandarin exhibits the lowest scores in both categories: 0.87 parsing F1 and 0.84 intervention F1, suggesting room for improvement in very low-resource or complex linguistic settings. In addition to F1 metrics, we examined BLEU scores for structured frame generation across languages. BLEU showed a similar downward trend: highest for English (0.89) and Spanish (0.87), slightly lower for Romanian (0.85) and Arabic (0.84), and lowest for Mandarin (0.81). This pattern reinforces the observation that the quality of structured output degrades modestly in low-resource or morphologically complex languages. However, the decline remains within an acceptable range for clinical use. To deepen the analysis, we investigated language-specific strengths and weaknesses. For instance, in English and Spanish, TriagE-NLU accurately captured both onset time and symptom structure even in cases with nested clauses, such as the following:  
\"My husband began slurring his speech about 15 min ago after taking his meds.\"  
In contrast, in Mandarin, parsing errors occurred more frequently for temporally ambiguous expressions, e.g., \"\(\exists\)\  
metadata, semantic parsing, classification, knowledge constraints, and federated learning is essential for robust multilingual clinical understanding and intervention planning. We define the Full Model or the baseline configuration as the full TriagE-NLU model, with all major components active: patient metadata integration, semantic parsing head (EUREKA slot-filling), intervention classification head, knowledge encoding and constraint enforcement, and federated update loop. We then evaluate the system under five ablation conditions, each designed to isolate the impact of one architectural component. In the \"no metadata condition\", age and co-morbidity inputs are removed from the input sequence. The \"no semantic parsing head\" variant disables structured EUREKA slot-filling, preserving only intervention classification. In contrast, the \"no classification head\" setup omits intervention prediction entirely, allowing the model to perform only semantic parsing. The \"no knowledge constraints\" configuration removes ontology-based validation and constraint enforcement in the outputs. Finally, the \"no federated learning\" condition replaces personalized, distributed updates with a centralized training strategy. Each variant is trained under identical settings and evaluated using the same test set. We present Slot F1, Frame F1, Macro F1 (intervention), and accuracy (intervention) to measure the impact of each component removed. Slot F1 measures the F1 score for individual semantic slots extracted by the model during the parsing process. Frame F1 measures the F1 score for the entire structured frame (e.g., the EUREKA frame). Macro F1 (intervention) measures the unweighted average of F1 scores across all intervention classes (e.g., \"activate stroke protocol\", \"monitor only\"). Accuracy (intervention) measures the percentage of intervention predictions that exactly match the ground truth. After discussing each ablation study, a comparison of the obtained scores will be provided and analyzed.