## 5 Training Methodology
### Data Preparation  
The data used in this study were generated synthetically to simulate multilingual emergency teleconsultation dialogues. No real patient data or teleconsultation transcripts were collected, and therefore, no ethical approval was required. The simulated dialogues were constructed using Python 3.11.9 scripts that systematically combine predefined medical templates, symptom lists, temporal expressions, and demographic metadata. These templates were inspired by clinical scenarios commonly encountered in emergency medicine (e.g., stroke, asthma, myocardial infarction) and encoded in five target languages: English, Spanish, Romanian, Arabic, and Mandarin.  
Each synthetic sample consists of a text input between a virtual clinician and a patient or caregiver. The dialogue includes naturalistic utterances aligned with structured medical information, such as symptoms, onset times, comorbidities, and risk factors. The corresponding ground truth annotations were programmatically generated alongside each sample using the proposed EUREKA schema, allowing automatic production of semantically structured representations. Intervention labels were assigned similarly based on deterministic mapping rules that link recognized symptom patterns with established triage protocols (e.g., activating the stroke protocol when slurred speech and unilateral arm weakness occur within 30 min of onset).  
To enhance linguistic variability, multilingual text augmentation strategies were applied to introduce variation in phrasing, word order, and use of regional expressions. While no real teleconsultation recordings were used, the synthetic corpus was designed to preserve clinical plausibility and cover a diverse range of urgent care scenarios. This approach enables large-scale, multilingual training without compromising privacy or data protection standards.  
The criteria for weakly labeled multilingual data generation and cross-lingual projection in our approach begin with the construction of annotated samples in English using predefined symptom templates aligned with the EUREKA schema. These annotations, including symptom spans and urgency indicators, are then projected to the target languages by preserving their semantic roles through deterministic inheritance. To ensure accuracy, token-level alignment techniques, such as attention maps from multilingual transformer models or external alignment tools, were applied to map the English labels to corresponding tokens in the translated text. Additionally, language-specific rules were implemented to correct syntactic variations, such as word order and prepositional structures, thereby maintaining grammatical and semantic consistency. Although the data remains fully synthetic, a portion of each generated dataset underwent manual spot-checking to validate linguistic plausibility and annotation quality. This process ensures the reproducibility and structural consistency of multilingual annotations across languages.  
The resulting dataset included fully synthetic dialogues built from clinical templates and encoded triage rules, as well as weakly labeled multilingual samples projected from English using rule-based extractors and cross-lingual mapping. Patient-specific metadata, such as age, comorbidities, and vital signs, was programmatically injected as auxiliary features. All inputs were normalized and tokenized using subword tokenization compatible with multilingual transformer models, ensuring consistency across languages and modalities.