# Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability

arXiv: https://arxiv.org/abs/1906.07601 · HTML: https://mlsanity.com/paper/1906.07601/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 29 of 29)

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Curriculum Temperature for Knowledge Distillation](https://mlsanity.com/paper/2211.16231/) (2022)
- [LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech](https://mlsanity.com/paper/2104.11462/) (2021)
- [Towards Reducing the Need for Speech Training Data To Build Spoken Language Understanding Systems](https://mlsanity.com/paper/2203.00006/) (2022)
- [LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech](https://mlsanity.com/paper/2309.05472/) (2023)
- [Where are we in semantic concept extraction for Spoken Language Understanding?](https://mlsanity.com/paper/2106.13045/) (2021)
- [End-to-end model for named entity recognition from speech without paired training data](https://mlsanity.com/paper/2204.00803/) (2022)
- [End2End Acoustic to Semantic Transduction](https://mlsanity.com/paper/2102.01013/) (2021)
- [Integrating Dialog History into End-to-End Spoken Language Understanding Systems](https://mlsanity.com/paper/2108.08405/) (2021)
- [RNN Transducer Models For Spoken Language Understanding](https://mlsanity.com/paper/2104.03842/) (2021)
- [On the Use of Semantically-Aligned Speech Representations for Spoken Language Understanding](https://mlsanity.com/paper/2210.05291/) (2022)
- [Representation based meta-learning for few-shot spoken intent recognition](https://mlsanity.com/paper/2106.15238/) (2021)
- [Toward Low-Cost End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2207.00352/) (2022)
- [End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting](https://mlsanity.com/paper/2207.08179/) (2022)
- [CBM: Curriculum by Masking](https://mlsanity.com/paper/2407.05193/) (2024)
- [MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs](https://mlsanity.com/paper/2202.12450/) (2022)
- [Semantic enrichment towards efficient speech representations](https://mlsanity.com/paper/2307.01323/) (2023)
- [A Comprehensive Study of Privacy Risks in Curriculum Learning](https://mlsanity.com/paper/2310.10124/) (2023)
- [Pantagruel: Unified Self-Supervised Encoders for French Text and Speech](https://mlsanity.com/paper/2601.05911/) (2026)
- [Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse Skills](https://mlsanity.com/paper/2303.15349/) (2023)
- [HuCurl: Human-induced Curriculum Discovery](https://mlsanity.com/paper/2307.07412/) (2023)
- [Leveraging Multilingual Self-Supervised Pretrained Models for Sequence-to-Sequence End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2310.06103/) (2023)
- [Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-smoothness in Deep GNNs](https://mlsanity.com/paper/2312.08221/) (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)
- [Improving End-to-End Models for Set Prediction in Spoken Language Understanding](https://mlsanity.com/paper/2201.12105/) (2022)
- [Vers la compr'ehension automatique de la parole bout-en-bout `a moindre effort](https://mlsanity.com/paper/2207.00349/) (2022)
- [Using Weak Supervision and Data Augmentation in Question Answering](https://mlsanity.com/paper/2309.16175/) (2023)
- [Curriculum Multi-Task Self-Supervision Improves Lightweight Architectures for Onboard Satellite Hyperspectral Image Segmentation](https://mlsanity.com/paper/2509.13229/) (2025)
- [Tone-Conditioned Curriculum Learning for Low-Resource Bantu Speech Recognition](https://mlsanity.com/paper/2606.31642/) (2026)

## Abstract (arXiv)

We present an end-to-end approach to extract semantic concepts directly from the speech audio signal. To overcome the lack of data available for this spoken language understanding approach, we investigate the use of a transfer learning strategy based on the principles of curriculum learning. This approach allows us to exploit out-of-domain data that can help to prepare a fully neural architecture. Experiments are carried out on the French MEDIA and PORTMEDIA corpora and show that this end-to-end SLU approach reaches the best results ever published on this task. We compare our approach to a classical pipeline approach that uses ASR, POS tagging, lemmatizer, chunker... and other NLP tools that aim to enrich ASR outputs that feed an SLU text to concepts system. Last, we explore the promising capacity of our end-to-end SLU approach to address the problem of domain portability.