## 7 Results and Performance Analysis
### TriagE-NLU Evaluation  
We evaluate TriagE-NLU using the TriageX benchmark described in Section 6. Our experiments assess model performance across three core tasks: semantic parsing into EUREKA frames, clinical intervention prediction, and multilingual generalization. We present results on simulated subsets of TriagX to evaluate generalization, robustness, and language transfer abilities. We split the TriageX dataset into training (70%), development (15%), and test (15%) sets, ensuring stratification across languages, urgency levels, and symptom combinations. To contextualize TriagE-NLU performance, we compare against strong baselines. * mT5-Base: Fine-tuned on TriageX using standard seq2 seq loss for EUREKA parsing only. * mBART: A multilingual encoder/decoder model. * MBART + MLP: A multilingual encoder/decoder model with an MLP classification head for intervention prediction. * XLM-RoBERTa: A multilingual language model. * XLM-RoBERTa + BiLSTM: Token-level slot tagging using a BiLSTM-CRF on top of frozen XLM-R embeddings. All models are implemented using Hugging Face Transformers and trained on an Intel Core i9 processor, 64 GB of RAM, and a GeForce RTX4070 Ti graphics card. Table 2 presents the results obtained for the performance evaluation of all models. TriagE-NLU achieves the highest scores across all available metrics, with 0.91 Slot F1, 0.89 Frame F1, 0.88 Macro F1 (Intervention), and 0.92 accuracy, demonstrating its balanced effectiveness in both structured semantic parsing and clinical prediction. In contrast, mT5-Base, a seq2 seq model, performs relatively well in parsing (0.84 Slot F1) but lacks support for intervention prediction (hence no Macro F1 reported), and achieves 0.85 accuracy. Models mBART and mBART + MLP achieve moderate performance in intervention classification, with Macro F1 scores of 0.81 and 0.83, respectively. However, they do not support structured semantic parsing. XLM-RoBERTa, with and without BiLSTM, shows the weakest overall results. The BiLSTM-enhanced variant achieves 0.79 Slot F1 and 0.78 accuracy, indicating its limited ability to capture frame-level semantics or make precise classifications. The base XLM-R model performs slightly better in classification (Macro F1: 0.80, accuracy: 0.82) but still underperforms compared to TriagE-NLU. In summary, Table 2 highlights TriagE-NLU's comprehensive and superior performance, especially its unique ability to handle both semantic parsing and intervention prediction in a unified multilingual setting, capabilities that baseline models support only partially or with lower accuracy. To provide a more intuitive visual comparison of the results obtained, we include Figure 7. In addition to Slot F1, Frame F1, Macro F1 (Intervention), and accuracy, we include the BLEU score, a sequence-level metric commonly used in machine translation to evaluate the quality of structured frame generation (EUREKA outputs). Figure 7 enables a more precise observation of performance differences across models and metrics, highlighting TriagE-NLU's consistent superiority over the baselines. While \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Slot F1** & **Frame F1** &
\begin{tabular}{c} **Macro F1** \\ **(Intervention)** \\ \end{tabular} & **Accuracy** \\ \hline TriagE-NLU & 0.91 & 0.89 & 0.88 & 0.92 \\ mT5-Base (Seq2 Seq) & 0.84 & \(-\) & \(-\) & 0.85 \\ mBART & \(-\) & \(-\) & 0.81 & 0.83 \\ mBART + MLP & \(-\) & \(-\) & 0.83 & 0.81 \\ XLM-ROBERTa & \(-\) & \(-\) & 0.80 & 0.82 \\ XLM-ROBERTa + BiLSTM & 0.79 & \(-\) & \(-\) & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative evaluation of TriagE-NLU and baseline models. Figure 7: Semantic parsing, intervention prediction, accuracy and BLEU comparison results between TriagE-NLU and baseline models. F1 and accuracy assess correctness in classification or slot-level predictions, BLEU measures how closely the generated sequences match ground-truth outputs in both content and word order.