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This paper proposes EGTA, an Evidence-Grounded Terminology Adaptation framework for simultaneous speech translation that selectively uses document-specific terminology to improve translation of rare terms, achieving significant gains in named-entity and acronym recall without full-model fine-tuning.
This paper describes the MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task, using Parakeet and Qwen 3.5 models with adaptive 'black-box' policies and a RAG mechanism for context, achieving significant quality improvements.