New Semantic Task for the French Spoken Language Understanding MEDIA
  Benchmark
Nadège Alavoine Gaëlle Laperriere Christophe Servan Sahar Ghannay Sophie Rosset
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.