# Where are we in semantic concept extraction for Spoken Language Understanding?

arXiv: https://arxiv.org/abs/2106.13045 · HTML: https://mlsanity.com/paper/2106.13045/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 24 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [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)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [Towards end-to-end spoken language understanding](https://mlsanity.com/paper/1802.08395/) (2018)
- [Building a Conversational Agent Overnight with Dialogue Self-Play](https://mlsanity.com/paper/1801.04871/) (2018)
- [End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results](https://mlsanity.com/paper/1412.1602/) (2014)
- [LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech](https://mlsanity.com/paper/2104.11462/) (2021)
- [Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability](https://mlsanity.com/paper/1906.07601/) (2019)
- [End2End Acoustic to Semantic Transduction](https://mlsanity.com/paper/2102.01013/) (2021)
- [ASR error management for improving spoken language understanding](https://mlsanity.com/paper/1705.09515/) (2017)

## Built on by (most cited first, 11 of 11)

- [LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech](https://mlsanity.com/paper/2309.05472/) (2023)
- [On the Use of Semantically-Aligned Speech Representations for Spoken Language Understanding](https://mlsanity.com/paper/2210.05291/) (2022)
- [ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks](https://mlsanity.com/paper/2207.01893/) (2022)
- [Toward Low-Cost End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2207.00352/) (2022)
- [New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark](https://mlsanity.com/paper/2403.19727/) (2024)
- [Pantagruel: Unified Self-Supervised Encoders for French Text and Speech](https://mlsanity.com/paper/2601.05911/) (2026)
- [Leveraging Multilingual Self-Supervised Pretrained Models for Sequence-to-Sequence End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2310.06103/) (2023)
- [A dual task learning approach to fine-tune a multilingual semantic speech encoder for Spoken Language Understanding](https://mlsanity.com/paper/2406.12141/) (2024)
- [Vers la compr'ehension automatique de la parole bout-en-bout `a moindre effort](https://mlsanity.com/paper/2207.00349/) (2022)
- [Detecci'on Autom'atica de Patolog'ias en Notas Cl'inicas en Espa~nol Combinando Modelos de Lenguaje y Ontolog'ias M'edicos](https://mlsanity.com/paper/2410.00616/) (2024)
- [Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies](https://mlsanity.com/paper/2412.03176/) (2024)

## Abstract (arXiv)

Spoken language understanding (SLU) topic has seen a lot of progress these last three years, with the emergence of end-to-end neural approaches. Spoken language understanding refers to natural language processing tasks related to semantic extraction from speech signal, like named entity recognition from speech or slot filling task in a context of human-machine dialogue. Classically, SLU tasks were processed through a cascade approach that consists in applying, firstly, an automatic speech recognition process, followed by a natural language processing module applied to the automatic transcriptions. These three last years, end-to-end neural approaches, based on deep neural networks, have been proposed in order to directly extract the semantics from speech signal, by using a single neural model. More recent works on self-supervised training with unlabeled data open new perspectives in term of performance for automatic speech recognition and natural language processing. In this paper, we present a brief overview of the recent advances on the French MEDIA benchmark dataset for SLU, with or without the use of additional data. We also present our last results that significantly outperform the current state-of-the-art with a Concept Error Rate (CER) of 11.2%, instead of 13.6% for the last state-of-the-art system presented this year.