@inproceedings{karakanta-2026-beyond,
title = "Beyond post-editing: A project-based module on {MT} and {LLM} integration for trainee translators",
author = "Karakanta, Alina",
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-1.7/",
pages = "52--62",
abstract = "With the rapid technologisation of translation, skills beyond post-editing (PE), such as data literacy, technology evaluation, and critical engagement with AI-based tools are becoming essential competencies for trainee translators. This paper presents a syllabus for a translation technology module that equips MA Translation students with end-to-end technology assessment skills, from engine selection and domain adaptation to automatic and human evaluation, post-editing, and reporting results. Large language models are integrated throughout, as translation engines, as a basis for prompting and domain adaptation strategies, and as a source of explainable quality estimation signals. Students apply these skills in a simulated client scenario as project-based learning. Trends on students' engine selection and customisation preferences observed across three years suggest a gradual shift towards LLM-based tools, but traditional engines and built-in options remain a firm presence in their practices."
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<abstract>With the rapid technologisation of translation, skills beyond post-editing (PE), such as data literacy, technology evaluation, and critical engagement with AI-based tools are becoming essential competencies for trainee translators. This paper presents a syllabus for a translation technology module that equips MA Translation students with end-to-end technology assessment skills, from engine selection and domain adaptation to automatic and human evaluation, post-editing, and reporting results. Large language models are integrated throughout, as translation engines, as a basis for prompting and domain adaptation strategies, and as a source of explainable quality estimation signals. Students apply these skills in a simulated client scenario as project-based learning. Trends on students’ engine selection and customisation preferences observed across three years suggest a gradual shift towards LLM-based tools, but traditional engines and built-in options remain a firm presence in their practices.</abstract>
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%0 Conference Proceedings
%T Beyond post-editing: A project-based module on MT and LLM integration for trainee translators
%A Karakanta, Alina
%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 karakanta-2026-beyond
%X With the rapid technologisation of translation, skills beyond post-editing (PE), such as data literacy, technology evaluation, and critical engagement with AI-based tools are becoming essential competencies for trainee translators. This paper presents a syllabus for a translation technology module that equips MA Translation students with end-to-end technology assessment skills, from engine selection and domain adaptation to automatic and human evaluation, post-editing, and reporting results. Large language models are integrated throughout, as translation engines, as a basis for prompting and domain adaptation strategies, and as a source of explainable quality estimation signals. Students apply these skills in a simulated client scenario as project-based learning. Trends on students’ engine selection and customisation preferences observed across three years suggest a gradual shift towards LLM-based tools, but traditional engines and built-in options remain a firm presence in their practices.
%U https://aclanthology.org/2026.taitt-1.7/
%P 52-62
Markdown (Informal)
[Beyond post-editing: A project-based module on MT and LLM integration for trainee translators](https://aclanthology.org/2026.taitt-1.7/) (Karakanta, TAITT 2026)
ACL