@inproceedings{oliver-alvarez-vidal-2026-teaching,
title = "Teaching Machine Translation Technologies with {MTUUOC}",
author = "Oliver, Antoni and
Alvarez-Vidal, Sergi",
editor = {Kr{\"u}ger, Ralph and
Kenny, Dorothy and
Castilho, Sheila and
{\'A}lvarez-Vidal, Sergi and
Aranberri, Nora and
Ginel, Mar{\'i}a Isabel Rivas and
Hackenbuchner, Jani{\c{c}}a},
booktitle = "Proceedings of the 1st International Workshop on Teaching {AI}-Based Translation and Technologies ({TAITT} 2026)",
month = jun,
year = "2026",
address = "Tilburg, the Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.taitt-1.2/",
pages = "12--18",
abstract = "This paper presents the pedagogical integration of MTUOC, an open-source project developed at Universitat Oberta de Catalunya (UOC){---}a distance-learning institution{---}to facilitate the training, fine-tuning, and integration of Neural Machine Translation (NMT) and Large Language Models (LLMs). The project consists of a modular suite of tools designed to streamline complex technical workflows for translation purposes. These components are currently utilised across research, industry knowledge transfer, and formal education. Specifically, the tools have been successfully implemented in a Bachelor{'}s degree in Translation and Interpreting and a Master{'}s degree in Translation Technologies. Furthermore, a pilot open course based on this framework received significant interest, reaching over 100 participants. This paper outlines the core components of the project, discusses the teaching experiences gathered in asynchronous environments, and describes the organisation of a forthcoming open course scheduled for October 2026. The results suggest that providing students with accessible, high-level interfaces for AI-based translation technologies enhances their technical autonomy and professional readiness."
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%0 Conference Proceedings
%T Teaching Machine Translation Technologies with MTUUOC
%A Oliver, Antoni
%A Alvarez-Vidal, Sergi
%Y Krüger, Ralph
%Y Kenny, Dorothy
%Y Castilho, Sheila
%Y Álvarez-Vidal, Sergi
%Y Aranberri, Nora
%Y Ginel, María Isabel Rivas
%Y Hackenbuchner, Janiça
%S Proceedings of the 1st International Workshop on Teaching AI-Based Translation and Technologies (TAITT 2026)
%D 2026
%8 June
%I European Association for Machine Translation
%C Tilburg, the Netherlands
%F oliver-alvarez-vidal-2026-teaching
%X This paper presents the pedagogical integration of MTUOC, an open-source project developed at Universitat Oberta de Catalunya (UOC)—a distance-learning institution—to facilitate the training, fine-tuning, and integration of Neural Machine Translation (NMT) and Large Language Models (LLMs). The project consists of a modular suite of tools designed to streamline complex technical workflows for translation purposes. These components are currently utilised across research, industry knowledge transfer, and formal education. Specifically, the tools have been successfully implemented in a Bachelor’s degree in Translation and Interpreting and a Master’s degree in Translation Technologies. Furthermore, a pilot open course based on this framework received significant interest, reaching over 100 participants. This paper outlines the core components of the project, discusses the teaching experiences gathered in asynchronous environments, and describes the organisation of a forthcoming open course scheduled for October 2026. The results suggest that providing students with accessible, high-level interfaces for AI-based translation technologies enhances their technical autonomy and professional readiness.
%U https://aclanthology.org/2026.taitt-1.2/
%P 12-18
Markdown (Informal)
[Teaching Machine Translation Technologies with MTUUOC](https://aclanthology.org/2026.taitt-1.2/) (Oliver & Alvarez-Vidal, TAITT 2026)
ACL
- Antoni Oliver and Sergi Alvarez-Vidal. 2026. Teaching Machine Translation Technologies with MTUUOC. In Proceedings of the 1st International Workshop on Teaching AI-Based Translation and Technologies (TAITT 2026), pages 12–18, Tilburg, the Netherlands. European Association for Machine Translation.