###### Abstract  
Telemedicine in emergency contexts presents unique challenges, particularly in multilingual and low-resource settings where accurate, clinical understanding and triage decision support are critical. This paper introduces TriagE-NLU, a novel multilingual natural language understanding system designed to perform both semantic parsing and clinical intervention classification from emergency dialogues. The system is built on a federated learning architecture to ensure data privacy and adaptability across regions and is trained using TriageX, a synthetic, clinically grounded dataset covering five languages (English, Spanish, Romanian, Arabic, and Mandarin). TriagE-NLU integrates fine-tuned multilingual transformers with a hybrid rules-and-policy decision engine, enabling it to parse structured medical information (symptoms, risk factors, temporal markers) and recommend appropriate interventions based on recognized patterns. Evaluation against strong multilingual baselines, including mT5, mBART, and XLM-RoBERTa, demonstrates superior performance by TriagE-NLU, achieving F1 scores of 0.91 for semantic parsing and 0.89 for intervention classification, along with 0.92 accuracy and a BLEU score of 0.87. These results validate the system's robustness in multilingual emergency telehealth and its ability to generalize across diverse input scenarios. This paper establishes a new direction for privacy-preserving, AI-assisted triage systems. multilingual NLP; emergency telemedicine; semantic parsing; intervention classification; federated learning; low-resource languages; medical dialogue; privacy-preserving +
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swering [6], these systems are typically trained on retrospective clinical notes or structured datasets. They are not designed for multilingual dialogue in high-stakes contexts. Prior works in clinical NLP have focused primarily on medical dialogue using a prompt structure [7], while others explored federated model challenges and opportunities in the healthcare domain [8]. Furthermore, most existing models lack grounding in clinical intervention protocols or adaptability to regional practices, making instant deployment unreliable. Clinical natural language processing has traditionally focused on structured document analysis, including tasks like named-entity recognition (NER), de-identification, and concept normalization from clinical notes. More recent methods leverage deep learning to improve performance on tasks like relation extraction [9] and concept linking [10]. However, these systems are designed for retrospective, monolingual, and often static documents, lacking the capacity for fast interpretation or action-oriented dialogue understanding in emergencies. Task-oriented dialogue systems for healthcare have seen increasing interest, with systems supporting medication adherence [11], mental health screening [12], and patient triage via chatbots [13]. Some studies model medical dialogue as a sequence-to-sequence problem, while others build slot-filling or question-answering systems grounded in medical ontologies [14]. Yet, few have addressed the specific challenges of instantaneous, multi-role, and high-stakes emergency teleconsultation scenarios. Clinical decision support systems (CDSSs) traditionally rely on rule-based engines or structured clinical inputs [15]. More recent efforts incorporate neural models for suggesting treatments or interventions [16], but these often depend on EHR (Electronic Health Record) inputs. Few systems interpret natural language as a direct signal for triggering intervention, especially in live emergency contexts. TriagE-NLU differs by mapping parsed utterances to time-critical actions such as activating stroke or trauma protocols, grounded in both dialogue and patient metadata. Federated learning (FL) has emerged as a privacy-preserving paradigm for training models across decentralized institutions without data sharing.