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.

通过使用语言理解（SLU）的联合模型进行意图分类和槽位填充是一项关键任务。本文介绍了使用半自动方法获取增强版本的 MEDIA 数据集并利用联合模型对意图分类和槽位填充进行了实验的初步结果。