Elyadata/TARIC-SLU

TARIC-SLU: A Tunisian Benchmark Dataset For Spoken Language Understanding

The primary contributions of this work are as follows:

1. Release of the TARIC-SLU corpus: The very first resource of its kind designed for SLU within the Tunisian dialect
2. Comprehensive exposition of the semantic representations and annotation methodologies employed in the construction of the TARIC-SLU corpus.
3. Release of an open-source SpeechBrain recipe to build the semantic tasks inherent to the TARIC-SLU dataset.
4. Provides baseline results for Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Spoken Language Understanding (SLU) tasks, all derived from the TARIC-SLU corpus.

If you use TARIC-SLU in your research, please cite it using the following BibTeX entry:

@inproceedings{mdhaffar-etal-2024-taric-slu,
    title = "{TARIC}-{SLU}: A {T}unisian Benchmark Dataset for Spoken Language Understanding",
    author = "Mdhaffar, Salima  and Bougares, Fethi  and de Mori, Renato  and  Zaiem, Salah  and  Ravanelli, Mirco  and  Est{\`e}ve, Yannick",
    editor = "Calzolari, Nicoletta  and Kan, Min-Yen  and Hoste, Veronique  and Lenci, Alessandro  and Sakti, Sakriani  and Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1357",
    pages = "15606--15616"

#TARIC-SLU

Download link : https://demo-lia.univ-avignon.fr/taric-dataset/

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license: cc-by-nc-4.0