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    <abstract>Spoken Language Understanding (SLU) aims to extract the semantic information from the speech utterance of user queries. It is a core component in a task-oriented dialog system. With the spectacular progress of deep neural network models and the evolution of pre-trained language models, SLU has obtained significant breakthroughs. However, only a few high-resource languages have taken advantage of this progress due to the absence of SLU resources. In this paper, we seek to mitigate this obstacle by introducing SLURP-TN. This dataset was created by recording 55 native speakers uttering sentences in Tunisian dialect, manually translated from six SLURP domains. The result is an SLU Tunisian dialect dataset that comprises 4165 sentences recorded into around 5 hours of acoustic material. We also develop a number of Automatic Speech Recognition and SLU models exploiting SLUTP-TN. The Dataset and baseline models are available at: https://huggingface.co/datasets/Elyadata/SLURP-TN.</abstract>
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%0 Conference Proceedings
%T SLURP-TN : Resource for Tunisian Dialect Spoken Language Understanding
%A Elleuch, Haroun
%A Mdhaffar, Salima
%A Estève, Yannick
%A Bougares, Fethi
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F elleuch-etal-2026-slurp
%X Spoken Language Understanding (SLU) aims to extract the semantic information from the speech utterance of user queries. It is a core component in a task-oriented dialog system. With the spectacular progress of deep neural network models and the evolution of pre-trained language models, SLU has obtained significant breakthroughs. However, only a few high-resource languages have taken advantage of this progress due to the absence of SLU resources. In this paper, we seek to mitigate this obstacle by introducing SLURP-TN. This dataset was created by recording 55 native speakers uttering sentences in Tunisian dialect, manually translated from six SLURP domains. The result is an SLU Tunisian dialect dataset that comprises 4165 sentences recorded into around 5 hours of acoustic material. We also develop a number of Automatic Speech Recognition and SLU models exploiting SLUTP-TN. The Dataset and baseline models are available at: https://huggingface.co/datasets/Elyadata/SLURP-TN.
%R 10.63317/4m2ac973aco4
%U https://aclanthology.org/2026.lrec-1.119/
%U https://doi.org/10.63317/4m2ac973aco4
%P 1544-1552

Markdown (Informal)

[SLURP-TN : Resource for Tunisian Dialect Spoken Language Understanding](https://aclanthology.org/2026.lrec-1.119/) (Elleuch et al., LREC 2026)

• SLURP-TN : Resource for Tunisian Dialect Spoken Language Understanding (Elleuch et al., LREC 2026)

ACL

• Haroun Elleuch, Salima Mdhaffar, Yannick Estève, and Fethi Bougares. 2026. SLURP-TN : Resource for Tunisian Dialect Spoken Language Understanding. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 1544–1552, Palma de Mallorca, Spain. ELRA Language Resource Association.