---
title: "Alexis Conneau — all works — Elamur Research"
description: "All 37 works of Alexis Conneau in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/alexis-conneau/works/"
---
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# Alexis Conneau — all works

37 works · [← Alexis Conneau](https://md.research.elamur.ai/free/en/authors/alexis-conneau/)

1. **[Unsupervised Cross-lingual Representation Learning at Scale ↗](https://aclanthology.org/2020.acl-main.747/) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yet**Alexis Conneau et al.*2020 · 9,468 citations*
2. **[Cross-lingual Language Model Pretraining ↗](https://arxiv.org/abs/1901.07291) arXiv:1901.07291 · no summary yet**Guillaume Lample, Alexis Conneau*2019 · 2,832 citations*
3. **[XNLI: Evaluating Cross-lingual Sentence Representations ↗](https://aclanthology.org/D18-1269/) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yet**Alexis Conneau et al.*2018 · 1,885 citations*
4. **[Very Deep Convolutional Networks for Text Classification](https://md.research.elamur.ai/free/en/papers/conneau-2017-very-deep-convolutional-networks-text/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Alexis Conneau, Holger Schwenk, Loïc Barrault, et al.*2017 · 1,094 citations*
5. **[What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties](https://md.research.elamur.ai/free/en/papers/conneau-2018-what-you-can-cram-into/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alexis Conneau, German Kruszewski, Guillaume Lample, et al.*2018 · 1,045 citations*
6. **[Word Translation Without Parallel Data ↗](https://arxiv.org/abs/1710.04087) ICLR 2018 (arXiv:1710.04087) · no summary yet**Alexis Conneau et al.*2018 · 913 citations*
7. **[Phrase-Based & Neural Unsupervised Machine Translation ↗](https://aclanthology.org/D18-1549/) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yet**Guillaume Lample et al.*2018 · 910 citations*
8. **[Unsupervised Cross-Lingual Representation Learning for Speech Recognition ↗](https://doi.org/10.21437/interspeech.2021-329) Interspeech (USB) · no summary yet**Alexis Conneau et al.*2021 · 811 citations*
9. **[XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://md.research.elamur.ai/free/en/papers/babu-2021-xls-r-self-supervised-cross/) [Interspeech](https://md.research.elamur.ai/free/en/venues/interspeech-usb/)**Arun Babu, Changhan Wang, Andros Tjandra, et al.*2021 · 807 citations*
10. **[Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://md.research.elamur.ai/free/en/papers/conneau-2017-supervised-learning-universal-sentence-representations/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alexis Conneau, Douwe Kiela, Holger Schwenk, et al.*2017 · 795 citations*
11. **[SentEval: An Evaluation Toolkit for Universal Sentence Representations](https://md.research.elamur.ai/free/en/papers/conneau-2018-senteval-evaluation-toolkit-universal-sentence/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Alexis Conneau, Douwe Kiela*2018 · 747 citations*
12. [**Unsupervised Machine Translation Using Monolingual Corpora Only arXiv:1711.00043**Guillaume Lample, Alexis Conneau, Ludovic Denoyer, et al.*2017 · 669 citations*](https://md.research.elamur.ai/free/en/papers/lample-2017-unsupervised-machine-translation-using-monolingual/)
13. **[CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://md.research.elamur.ai/free/en/papers/wenzek-2020-ccnet-extracting-high-quality-monolingual/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, et al.*2020 · 666 citations*
14. **[FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://md.research.elamur.ai/free/en/papers/conneau-2022-fleurs-few-shot-learning-evaluation/) [IEEE Spoken Language Technology Workshop (SLT)](https://md.research.elamur.ai/free/en/venues/ieee-spoken-language-technology-workshop-slt/)**Alexis Conneau, Min Ma, Simran Khanuja, et al.*2022 · 405 citations*
15. **[Emerging Cross-lingual Structure in Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/conneau-2020-emerging-cross-lingual-structure-pretrained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alexis Conneau, Shijie Wu, Haoran Li, et al.*2020 · 382 citations*
16. **[Very Deep Convolutional Networks for Natural Language Processing. ↗](https://128.84.21.199/abs/1606.01781) arXiv (Cornell University) · no summary yet**Alexis Conneau et al.*2016 · 263 citations*
17. [**Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning arXiv:2011.01403**Beliz Gunel, Jingfei Du, Alexis Conneau, et al.*2020 · 217 citations*](https://md.research.elamur.ai/free/en/papers/gunel-2020-supervised-contrastive-learning-pre-trained/)
18. **[Meta-Prod2Vec ↗](https://doi.org/10.1145/2959100.2959160) ACM Conference on Recommender Systems (RecSys) · no summary yet**Flavian Vasile, Elena Smirnova, Alexis Conneau*2016 · 177 citations*
19. **[Self-training Improves Pre-training for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/du-2021-self-training-improves-pre-training/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jingfei Du, Edouard Grave, Beliz Gunel, et al.*2021 · 172 citations*
20. **[Larger-Scale Transformers for Multilingual Masked Language Modeling](https://md.research.elamur.ai/free/en/papers/goyal-2021-larger-scale-transformers-multilingual-masked/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Naman Goyal, Jingfei Du, Myle Ott, et al.*2021 · 161 citations*
21. **[Self-Training and Pre-Training are Complementary for Speech Recognition ↗](https://doi.org/10.1109/icassp39728.2021.9414641) IEEE International Conference on Acoustics Speech and Signal Processing · no summary yet**Qiantong Xu et al.*2021 · 159 citations*
22. **[Multilingual Speech Translation from Efficient Finetuning of Pretrained Models ↗](https://aclanthology.org/2021.acl-long.68/) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yet**Xian Li et al.*2021 · 138 citations*
23. **[Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation ↗](https://arxiv.org/abs/1607.07326) arXiv (Cornell University) · no summary yet**Flavian Vasile, Smirnova, Elena, Alexis Conneau*2016 · 119 citations*
24. **[Learning Visually Grounded Sentence Representations ↗](https://aclanthology.org/N18-1038/) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · no summary yet**Douwe Kiela et al.*2018 · 102 citations*
25. **[mSLAM: Massively multilingual joint pre-training for speech and text ↗](https://arxiv.org/abs/2202.01374) arXiv (Cornell University) · no summary yet**Ankur Bapna et al.*2022 · 99 citations*
26. **[SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training ↗](https://arxiv.org/abs/2110.10329) arXiv (Cornell University) · no summary yet**Ankur Bapna et al.*2021 · 72 citations*
27. **[Unsupervised Speech Recognition ↗](https://arxiv.org/abs/2105.11084) arXiv (Cornell University) · no summary yet**Alexei Baevski et al.*2021 · 62 citations*
28. **[Textually Pretrained Speech Language Models ↗](https://doi.org/10.52202/075280-2771) neural information processing systems · no summary yet**Michael Hassid et al.*2023 · 54 citations*
29. [**Scaling Laws for Generative Mixed-Modal Language Models arXiv:2301.03728**Armen Aghajanyan, Lili Yu, Alexis Conneau, et al.*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/aghajanyan-2023-scaling-laws-generative-mixed-modal/)
30. **[Multilingual Speech Translation with Efficient Finetuning of Pretrained Models ↗](https://arxiv.org/abs/2010.12829) arXiv (Cornell University) · no summary yet**Xian Li et al.*2020 · 43 citations*
31. **[Improved Language Identification Through Cross-Lingual Self-Supervised Learning ↗](https://doi.org/10.1109/icassp43922.2022.9747667) IEEE International Conference on Acoustics Speech and Signal Processing · no summary yet**Andros Tjandra et al.*2022 · 41 citations*
32. **[Large-Scale Self- and Semi-Supervised Learning for Speech Translation ↗](https://doi.org/10.21437/interspeech.2021-1912) Interspeech (USB) · no summary yet**Changhan Wang et al.*2021 · 34 citations*
33. **[XTREME-S: Evaluating Cross-lingual Speech Representations ↗](https://doi.org/10.21437/interspeech.2022-10007) Interspeech (USB) · no summary yet**Alexis Conneau et al.*2022 · 28 citations*
34. **[Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation ↗](https://doi.org/10.21437/interspeech.2022-10938) Interspeech (USB) · no summary yet**Jia Ye et al.*2022 · 27 citations*
35. **[Toward Joint Language Modeling for Speech Units and Text ↗](https://aclanthology.org/2023.findings-emnlp.438/) Findings of the Association for Computational Linguistics: EMNLP 2023 · no summary yet**Ju-Chieh Chou et al.*2023 · 23 citations*
36. **[Apprentissage et applications de représentations multilingues distribuées ↗](https://openalex.org/W3023305215) theses.fr (ABES) · no summary yet**Alexis Conneau*2019*
37. **[Learning distributed representations of sentences using neural networks ↗](https://doi.org/10.70675/b3ecf08ez353ez4532zb829z7c06c26a7e07) no summary yet**Alexis Conneau*2019*