@inproceedings{barriga-martinez-etal-2026-mapudungun,
title = "{M}apudungun-{S}panish Speech Translation: A Low-Resource End-to-End System for the {IWSLT} 2026 Shared Task",
author = "Barriga Mart{\'i}nez, Diego Alberto and
Gazque, Amilkar and
Segura Elizalde, Mikel and
Hernandez Mena, Carlos Daniel and
Gutierrez-Vasques, Ximena and
Meza Ruiz, Ivan Vladimir",
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-1.26/",
pages = "232--237",
ISBN = "979-8-89176-411-8",
abstract = "We present an end-to-end speech translation system for Mapudungun{--}Spanish developed for the IWSLT 2026 low-resource task. Building on the Canary-1B-v2 model, we apply parameter-efficient fine-tuning with a lightweight adapter and leverage an English-centered configuration as a proxy to enable translation. Experiments show that the system captures key phonetic patterns despite limited data, though it exhibits biases toward repetitive Spanish outputs. Our results highlight both the feasibility and the challenges of adapting multilingual foundation models to low-resource Indigenous languages."
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<abstract>We present an end-to-end speech translation system for Mapudungun–Spanish developed for the IWSLT 2026 low-resource task. Building on the Canary-1B-v2 model, we apply parameter-efficient fine-tuning with a lightweight adapter and leverage an English-centered configuration as a proxy to enable translation. Experiments show that the system captures key phonetic patterns despite limited data, though it exhibits biases toward repetitive Spanish outputs. Our results highlight both the feasibility and the challenges of adapting multilingual foundation models to low-resource Indigenous languages.</abstract>
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%0 Conference Proceedings
%T Mapudungun-Spanish Speech Translation: A Low-Resource End-to-End System for the IWSLT 2026 Shared Task
%A Barriga Martínez, Diego Alberto
%A Gazque, Amilkar
%A Segura Elizalde, Mikel
%A Hernandez Mena, Carlos Daniel
%A Gutierrez-Vasques, Ximena
%A Meza Ruiz, Ivan Vladimir
%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 barriga-martinez-etal-2026-mapudungun
%X We present an end-to-end speech translation system for Mapudungun–Spanish developed for the IWSLT 2026 low-resource task. Building on the Canary-1B-v2 model, we apply parameter-efficient fine-tuning with a lightweight adapter and leverage an English-centered configuration as a proxy to enable translation. Experiments show that the system captures key phonetic patterns despite limited data, though it exhibits biases toward repetitive Spanish outputs. Our results highlight both the feasibility and the challenges of adapting multilingual foundation models to low-resource Indigenous languages.
%U https://aclanthology.org/2026.iwslt-1.26/
%P 232-237
Markdown (Informal)
[Mapudungun-Spanish Speech Translation: A Low-Resource End-to-End System for the IWSLT 2026 Shared Task](https://aclanthology.org/2026.iwslt-1.26/) (Barriga Martínez et al., IWSLT 2026)
ACL