mlsanityData to 7 Oct 2026

New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark

arXiv 2403.19727 ↗First posted 28 Mar 2024cs.CL cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 51 references that are in the map. First posted on arXiv 28 Mar 2024.

Builds on

21 of 51 references.

References · 21

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9 more
  1. 2022SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representationcited by 32
  2. 2019Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Modelcited by 24
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  5. 2022Bi-directional Joint Neural Networks for Intent Classification and Slot Fillingcited by 8
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Citing papers per year: 2025: 2.

2 papers.

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  2. 2025Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learningcited by 0