# NAVER LABS Europe's Multilingual Speech Translation Systems for the IWSLT 2023 Low-Resource Track

arXiv: https://arxiv.org/abs/2306.07763 · HTML: https://mlsanity.com/paper/2306.07763/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 13 Jun 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://mlsanity.com/paper/2008.00401/) (2020)
- [Simple, Scalable Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1909.08478/) (2019)
- [The Multilingual TEDx Corpus for Speech Recognition and Translation](https://mlsanity.com/paper/2102.01757/) (2021)
- [SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation](https://mlsanity.com/paper/2205.08180/) (2022)
- [Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters](https://mlsanity.com/paper/2110.09574/) (2021)
- [Speech Resources in the Tamasheq Language](https://mlsanity.com/paper/2201.05051/) (2022)
- [Continual Learning in Multilingual NMT via Language-Specific Embeddings](https://mlsanity.com/paper/2110.10478/) (2021)
- [Efficient Inference for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2109.06679/) (2021)
- [FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task](https://mlsanity.com/paper/2107.06959/) (2021)
- [ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks](https://mlsanity.com/paper/2205.01987/) (2022)

## Built on by (most cited first, 9 of 9)

- [Multilingual DistilWhisper: Efficient Distillation of Multi-task Speech Models via Language-Specific Experts](https://mlsanity.com/paper/2311.01070/) (2023)
- [Pushing the Limits of Zero-shot End-to-End Speech Translation](https://mlsanity.com/paper/2402.10422/) (2024)
- [From TOWER to SPIRE: Adding the Speech Modality to a Translation-Specialist LLM](https://mlsanity.com/paper/2503.10620/) (2025)
- [Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems](https://mlsanity.com/paper/2404.01616/) (2024)
- [SpeechMapper: Speech-to-text Embedding Projector for LLMs](https://mlsanity.com/paper/2601.20417/) (2026)
- [How To Build Competitive Multi-gender Speech Translation Models For Controlling Speaker Gender Translation](https://mlsanity.com/paper/2310.15114/) (2023)
- [Efficient Compression of Multitask Multilingual Speech Models](https://mlsanity.com/paper/2405.00966/) (2024)
- [IIITH-BUT system for IWSLT 2025 low-resource Bhojpuri to Hindi speech translation](https://mlsanity.com/paper/2506.04714/) (2025)
- [An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks](https://mlsanity.com/paper/2406.14747/) (2024)

## Abstract (arXiv)

This paper presents NAVER LABS Europe's systems for Tamasheq-French and Quechua-Spanish speech translation in the IWSLT 2023 Low-Resource track. Our work attempts to maximize translation quality in low-resource settings using multilingual parameter-efficient solutions that leverage strong pre-trained models. Our primary submission for Tamasheq outperforms the previous state of the art by 7.5 BLEU points on the IWSLT 2022 test set, and achieves 23.6 BLEU on this year's test set, outperforming the second best participant by 7.7 points. For Quechua, we also rank first and achieve 17.7 BLEU, despite having only two hours of translation data. Finally, we show that our proposed multilingual architecture is also competitive for high-resource languages, outperforming the best unconstrained submission to the IWSLT 2021 Multilingual track, despite using much less training data and compute.