## 7 Results and Performance Analysis
### Multilingual Transfer and Zero-Shot Generalization
To ensure a fair comparison across all ablation variants and the full model, we maintained consistent training configurations for each experiment. All models were fine-tuned using the google/mT5-Base backbone with a maximum sequence length of 256 tokens, a batch size of eight, and a learning rate of \(3\times 10^{-5}\). We used the AdamW optimizer with linear learning rate scheduling and warm-up steps set to 10% of the total training steps. Each model was trained for three epochs on the same training and development splits of the TriagE-X dataset. We employed early stopping based on validation loss with a patience of 1 epoch to prevent overfitting. The experiments are detailed below. Full model (TriagE-NLU). The results achieved with the full model are 0.91 Slot F1 score, 0.89 Frame F1 score, 0.88 Macro F1 (intervention) and 0.92 accuracy. Ablation Study 1. Without Patient Metadata. We exclude structured patient background information, specifically age and pre-existing conditions, from the input sequence. This configuration tests the model's ability to perform semantic parsing and intervention classification based solely on the raw clinical dialogue, without the contextual enrichment provided by patient demographics and comorbidities. In the baseline setup, metadata is appended using special delimiters (e.g., [SEP]) to help the model jointly encode both free-text utterances and structured medical context. By removing this metadata, we assess the extent to which such auxiliary information contributes to the accuracy and precision of symptom extraction and intervention decisions. This ablation is significant in evaluating performance when metadata may be unavailable or incomplete. In this experiment, the model achieved a Slot F1 score of 0.82, down from 0.91 in the full model. The Frame F1 score dropped from 0.89 to 0.78, indicating a lower ability to assemble consistent structured representations. On the intervention prediction task, the model recorded a Macro F1 (intervention) of 0.75 and accuracy of 0.81, reflecting reduced reliability in classifying appropriate actions due to missing patient context. A failed example in this experiment is where the input states: \"She's dizzy and can't feel her arm.\". Without metadata, the model fails to prioritize stroke as the primary diagnosis and instead recommends general observation or a low-urgency triage category. However, in the full model, with metadata indicating that the patient is 75 years old with a history of stroke and diabetes, TriagE-NLU correctly identifies this as a high-risk stroke scenario and recommends activating the stroke protocol immediately. This illustrates how the absence of metadata could lead to an underestimation of urgency and incorrect intervention. The integration of patient metadata is essential to the architecture as it provides vital contextual cues that significantly enhance the model's ability to accurately interpret symptoms, construct consistent clinical frames, and recommend appropriate interventions, capabilities that diminish notably when this component is removed. Ablation Study 2. Without Semantic Parsing Head. In this configuration, the semantic parsing head responsible for extracting structured EUREKA frames is disabled, and the model only performs intervention classification based on unstructured input. This design eliminates the ability to explicitly capture fine-grained clinical details, such as symptom types, onset times, or risk indicators. As a result, the model operates as a black-box classifier, lacking intermediate symbolic reasoning. Performance evaluation revealed a noticeable decline in interpretability, although the Macro F1 (intervention) remained relatively stable at 0.87 and accuracy 0.90. However, Slot F1 and Frame F1 metrics could not be computed due to the absence of structured outputs. This highlights the crucial role of the parsing head not only for downstream explainability but also for enabling fine-grained medical documentation, rule-based post-processing, and clinical auditability.