# New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark

arXiv: https://arxiv.org/abs/2403.19727 · HTML: https://mlsanity.com/paper/2403.19727/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 51 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Population Based Training of Neural Networks](https://mlsanity.com/paper/1711.09846/) (2017)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [End-to-End Slot Alignment and Recognition for Cross-Lingual NLU](https://mlsanity.com/paper/2004.14353/) (2020)
- [A Co-Interactive Transformer for Joint Slot Filling and Intent Detection](https://mlsanity.com/paper/2010.03880/) (2020)
- [LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech](https://mlsanity.com/paper/2104.11462/) (2021)
- [Strong Baselines for Neural Semi-supervised Learning under Domain Shift](https://mlsanity.com/paper/1804.09530/) (2018)
- [SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation](https://mlsanity.com/paper/2205.08180/) (2022)
- [Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model](https://mlsanity.com/paper/1907.02884/) (2019)
- [Where are we in semantic concept extraction for Spoken Language Understanding?](https://mlsanity.com/paper/2106.13045/) (2021)
- [End2End Acoustic to Semantic Transduction](https://mlsanity.com/paper/2102.01013/) (2021)
- [Bi-directional Joint Neural Networks for Intent Classification and Slot Filling](https://mlsanity.com/paper/2202.13079/) (2022)
- [On the Usability of Transformers-based models for a French Question-Answering task](https://mlsanity.com/paper/2207.09150/) (2022)
- [ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks](https://mlsanity.com/paper/2207.01893/) (2022)
- [Benchmarking Transformers-based models on French Spoken Language Understanding tasks](https://mlsanity.com/paper/2207.09152/) (2022)
- [Semantic enrichment towards efficient speech representations](https://mlsanity.com/paper/2307.01323/) (2023)

## Built on by (most cited first, 2 of 2)

- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning](https://mlsanity.com/paper/2506.03035/) (2025)

## 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.