## 5 Training Methodology
### Cross-Lingual Alignment and Adaptation  
TriagE-NLU is designed to operate across linguistically diverse environments, enabling semantic parsing and triage prediction in multiple languages. To facilitate cross-lingual generalization, we leverage the mT5-Base model, a multilingual sequence-to-sequence transformer pretrained on a large corpus of over 100 languages. This language-agnostic pretraining provides robust encoder representations that support transfer learning in both high-resource and low-resource language settings.  
A key component of our cross-lingual strategy is the use of a shared EUREKA slot ontology, which defines a standard set of clinical entities and attributes (e.g., symptoms, onset time, urgency, comorbidities, interventions) in a language-independent manner. All training samples, regardless of language, are mapped to this unified schema. This shared structure ensures that the model learns a consistent semantic frame across different linguistic inputs, reducing language-specific variability in output formatting and facilitating transfer.  
During training, we first construct annotated examples in English and then project these annotations to other target languages through translation and alignment. To maintain semantic integrity during projection, we employ token-level alignment using attention maps and heuristic rules that account for language-specific syntax. These aligned labels are used to train the multilingual T5 model to generate EUREKA-compliant frames across all supported languages.  
The EUREKA schema itself serves as a cross-lingual anchor, enabling the model to generalize from syntactic surface forms to more profound clinical semantics. This design is particularly effective in low-resource languages, where labeled data is limited, but the structured nature of the output provides a stable target. For instance, in Romanian and Arabic, languages with fewer pretraining resources, the model achieves only a minor degradation in frame accuracy.  
We also observed that constrained decoding plays a stabilizing role in low-resource settings by enforcing output structure. Even when token-level errors occur due to noise in the input, the decoder frequently recovers valid EUREKA frames thanks to schema-aware slot constraints.  
Most of the errors observed were due to mild misalignments in symptom phrasing or ambiguity in translated onset expressions, for example, in Romanian \"aproximativ acum treizeci de minute\" (\"around thirty minutes ago\") being misinterpreted as a broader time range. Despite these minor discrepancies, the intervention classification accuracy remained stable across languages, with a deviation of less than 5% from the English baseline. Constrained decoding was particularly effective in these settings, often recovering valid EUREKA frames even when token-level noise or partial input loss occurred. For example, in the Arabic dialogue:  
(\"The patient feels dizzy and has been unable to move his right hand for about ten minutes.\")  
Triage-NLU correctly produced symptoms (dizziness, right arm weakness), onset_time (10 min), urgency (critical) and intervention (activate stroke protocol).  
Overall, Triag-NLU achieves robust multilingual performance by combining language-agnostic encoder representations, shared schema supervision, and alignment-aware training techniques. This approach enables consistent clinical interpretation across diverse linguistic contexts, a critical requirement for scalable emergency triage systems.