@inproceedings{plum-etal-2026-ltzglue,
title = "ltz{GLUE}: {L}uxembourgish General Language Understanding Evaluation",
author = {Plum, Alistair and
K{\"o}rner, Felicia and
Lutgen, Anne-Marie and
Bernardy, Laura and
Philippy, Fred and
Milano, Emilia and
Rehlinger, Nils and
Lothritz, Cedric and
Ranasinghe, Tharindu and
Plank, Barbara and
Purschke, Christoph},
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.476/",
pages = "9791--9807",
ISBN = "979-8-89176-395-1",
abstract = "This paper presents ltzGLUE, the first Natural Language Understanding (NLU) benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for English. Although NLU tasks are available for many european languages nowadays, LTZ is one of the official national languages that is often overlooked. We introduce new tasks and reuse existing ones to introduce the first official NLU benchmark and accompanying evaluation of encoder models for the language. Our tasks include common natural language processing tasks in binary and multi-class classification settings, including named entity recognition, topic classification, and intent classification. We evaluate various pre-trained language models for LTZ to present an overview of the current capabilities of these models on the LTZ language."
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<abstract>This paper presents ltzGLUE, the first Natural Language Understanding (NLU) benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for English. Although NLU tasks are available for many european languages nowadays, LTZ is one of the official national languages that is often overlooked. We introduce new tasks and reuse existing ones to introduce the first official NLU benchmark and accompanying evaluation of encoder models for the language. Our tasks include common natural language processing tasks in binary and multi-class classification settings, including named entity recognition, topic classification, and intent classification. We evaluate various pre-trained language models for LTZ to present an overview of the current capabilities of these models on the LTZ language.</abstract>
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%0 Conference Proceedings
%T ltzGLUE: Luxembourgish General Language Understanding Evaluation
%A Plum, Alistair
%A Körner, Felicia
%A Lutgen, Anne-Marie
%A Bernardy, Laura
%A Philippy, Fred
%A Milano, Emilia
%A Rehlinger, Nils
%A Lothritz, Cedric
%A Ranasinghe, Tharindu
%A Plank, Barbara
%A Purschke, Christoph
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F plum-etal-2026-ltzglue
%X This paper presents ltzGLUE, the first Natural Language Understanding (NLU) benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for English. Although NLU tasks are available for many european languages nowadays, LTZ is one of the official national languages that is often overlooked. We introduce new tasks and reuse existing ones to introduce the first official NLU benchmark and accompanying evaluation of encoder models for the language. Our tasks include common natural language processing tasks in binary and multi-class classification settings, including named entity recognition, topic classification, and intent classification. We evaluate various pre-trained language models for LTZ to present an overview of the current capabilities of these models on the LTZ language.
%U https://aclanthology.org/2026.findings-acl.476/
%P 9791-9807
Markdown (Informal)
[ltzGLUE: Luxembourgish General Language Understanding Evaluation](https://aclanthology.org/2026.findings-acl.476/) (Plum et al., Findings 2026)
ACL
- Alistair Plum, Felicia Körner, Anne-Marie Lutgen, Laura Bernardy, Fred Philippy, Emilia Milano, Nils Rehlinger, Cedric Lothritz, Tharindu Ranasinghe, Barbara Plank, and Christoph Purschke. 2026. ltzGLUE: Luxembourgish General Language Understanding Evaluation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 9791–9807, San Diego, California, United States. Association for Computational Linguistics.