New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark
<|reference_start|>New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark: Intent classification and slot-filling are essential tasks of Spoken Language Understanding (SLU). In most SLUsystems, those tasks are realized by independent modules. For about fifteen years, models achieving both of themjointly and exploiting their mutual enhancement have been proposed. A multilingual module using a joint modelwas envisioned to create a touristic dialogue system for a European project, HumanE-AI-Net. A combination ofmultiple datasets, including the MEDIA dataset, was suggested for training this joint model. The MEDIA SLU datasetis a French dataset distributed since 2005 by ELRA, mainly used by the French research community and free foracademic research since 2020. Unfortunately, it is annotated only in slots but not intents. An enhanced version ofMEDIA annotated with intents has been built to extend its use to more tasks and use cases. This paper presents thesemi-automatic methodology used to obtain this enhanced version. In addition, we present the first results of SLUexperiments on this enhanced dataset using joint models for intent classification and slot-filling.<|reference_end|>
@article{alavoine2024new,
    title={New Semantic Task for the French Spoken Language Understanding MEDIA
  Benchmark},
    author={Nad`ege Alavoine (STL), Ga"elle Laperriere (LIA), Christophe Servan
  (STL), Sahar Ghannay (STL), Sophie Rosset (STL)},
    journal={The 2024 Joint International Conference on Computational
  Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 2024,
  Torino, Italy},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19727},
    primaryClass={cs.CL cs.AI}
}