MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task
This paper describes the MLLP-VRAIN UPV system for IWSLT 2026 Simultaneous Speech Translation, using Parakeet and Qwen 3.5 models with adaptive black-box policies to improve quality-latency trade-offs. The system participates in all language directions and introduces a context track for En→De, It, Zh using ASR word-boosting and RAG. Results show a +5.82 XCOMET-XL improvement on MCIF En→De, with additional +1.03 from context processing.
[2606.17255] MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task
[Submitted on 15 Jun 2026]
Title:MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task
View a PDF of the paper titled MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task, by Jorge Iranzo-S\'anchez and Gerard Mas-Molla and Adria Gim\'enez and Jorge Civera and Albert Sanchis and Alfons Juan
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Abstract:This work describes the participation of the MLLP-VRAIN research group in the shared task of the IWSLT 2026 Simultaneous Speech Translation track. Our submission utilizes the recently released Parakeet and Qwen 3.5 models to create a robust, cascaded solution for long-form SimulST through the use of adaptive "black-box" policies. We explore relaxations of these policies to achieve better quality-latency trade-offs. Compared to last year, we participate on all language directions. In addition to this, for the En$\rightarrow${De, It, Zh} directions we also participate in this year's new context track employing a combination of ASR word-boosting and a RAG mechanism of offline pre-translated exemplars to guide generation and enrich our system with domain-specific context. Finally, we provide a detailed latency analysis of our system. Compared to last year, results on the MCIF En$\rightarrow$De test set shows a substantial quality improvement of +5.82 XCOMET-XL. Our context track processing further improves performance by +1.03.
Comments: IWSLT 2026 System Description
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.17255 [cs.CL]
(or arXiv:2606.17255v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.17255
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jorge Iranzo-Sánchez [view email] [v1] Mon, 15 Jun 2026 19:57:05 UTC (464 KB)
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