@inproceedings{iranzo-sanchez-etal-2026-mllp,
title = "{MLLP}-{VRAIN} {UPV} System for the {IWSLT} 2026 Simultaneous Speech Translation Task",
author = "Iranzo-S{\'a}nchez, Jorge and
Mas-Moll{\`a}, Gerard and
Gimenez, Adri{\`a} and
Civera Saiz, Jorge and
Sanchis, Albert and
Juan, Alfons",
editor = "Salesky, Elizabeth and
Anastasopoulos, Antonios and
Negri, Matteo and
Federico, Marcello",
booktitle = "Proceedings of the 23rd International Conference on Spoken Language Translation ({IWSLT} 2026)",
month = jul,
year = "2026",
address = "San Diego, USA (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.iwslt-1.24/",
pages = "212--226",
ISBN = "979-8-89176-411-8",
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{\textrightarrow}{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{\textrightarrow}De test set shows a substantial quality improvement of +5.82 XCOMET-XL. Our context track processing further improves performance by +1.03."
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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→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→De test set shows a substantial quality improvement of +5.82 XCOMET-XL. Our context track processing further improves performance by +1.03.</abstract>
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%0 Conference Proceedings
%T MLLP-VRAIN UPV System for the IWSLT 2026 Simultaneous Speech Translation Task
%A Iranzo-Sánchez, Jorge
%A Mas-Mollà, Gerard
%A Gimenez, Adrià
%A Civera Saiz, Jorge
%A Sanchis, Albert
%A Juan, Alfons
%Y Salesky, Elizabeth
%Y Anastasopoulos, Antonios
%Y Negri, Matteo
%Y Federico, Marcello
%S Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, USA (in-person and online)
%@ 979-8-89176-411-8
%F iranzo-sanchez-etal-2026-mllp
%X 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→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→De test set shows a substantial quality improvement of +5.82 XCOMET-XL. Our context track processing further improves performance by +1.03.
%U https://aclanthology.org/2026.iwslt-1.24/
%P 212-226
Markdown (Informal)
[MLLP-VRAIN UPV System for the IWSLT 2026 Simultaneous Speech Translation Task](https://aclanthology.org/2026.iwslt-1.24/) (Iranzo-Sánchez et al., IWSLT 2026)
ACL
- Jorge Iranzo-Sánchez, Gerard Mas-Mollà, Adrià Gimenez, Jorge Civera Saiz, Albert Sanchis, and Alfons Juan. 2026. MLLP-VRAIN UPV System for the IWSLT 2026 Simultaneous Speech Translation Task. In Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026), pages 212–226, San Diego, USA (in-person and online). Association for Computational Linguistics.