| Unsupervised Cross-lingual Representation Learning at Scale A Conneau, K Khandelwal, N Goyal, V Chaudhary, G Wenzek, F Guzmán, ... ACL 2020, 2019 | 6605 | 2019 |
| Cross-lingual Language Model Pretraining A Conneau, G Lample NeurIPS 2019 (Spotlight), 2019 | 3328* | 2019 |
| Supervised Learning of Universal Sentence Representations from Natural Language Inference Data A Conneau, D Kiela, H Schwenk, L Barrault, A Bordes EMNLP 2017 (Outstanding Paper Award), 2017 | 2704 | 2017 |
| Word Translation Without Parallel Data A Conneau, G Lample, MA Ranzato, L Denoyer, H Jégou ICLR 2018, 2017 | 1945* | 2017 |
| Very Deep Convolutional Networks for Natural Language Processing A Conneau, H Schwenk, L Barrault, Y Lecun EACL 2017, 2016 | 1746* | 2016 |
| XNLI: Evaluating Cross-lingual Sentence Representations A Conneau, G Lample, R Rinott, A Williams, SR Bowman, H Schwenk, ... EMNLP 2018, 2018 | 1423 | 2018 |
| Unsupervised Machine Translation Using Monolingual Corpora Only G Lample, A Conneau, L Denoyer, MA Ranzato ICLR 2018, 2017 | 1347 | 2017 |
| What you can cram into a single vector: Probing sentence embeddings for linguistic properties A Conneau, G Kruszewski, G Lample, L Barrault, M Baroni ACL 2018, 2018 | 1030 | 2018 |
| Unsupervised cross-lingual representation learning for speech recognition A Conneau, A Baevski, R Collobert, A Mohamed, M Auli Interspeech 2021, 2020 | 850 | 2020 |
| Phrase-Based & Neural Unsupervised Machine Translation G Lample, M Ott, A Conneau, L Denoyer, MA Ranzato EMNLP 2018 (Best Paper Award), 2018 | 800 | 2018 |
| SentEval: An Evaluation Toolkit for Universal Sentence Representations A Conneau, D Kiela LREC 2018, 2018 | 782 | 2018 |
| XLS-R: Self-supervised cross-lingual speech representation learning at scale A Babu, C Wang, A Tjandra, K Lakhotia, Q Xu, N Goyal, K Singh, ... Interspeech 2022, 2021 | 665 | 2021 |
| Ccnet: Extracting high quality monolingual datasets from web crawl data G Wenzek, MA Lachaux, A Conneau, V Chaudhary, F Guzman, A Joulin, ... LREC 2020, 2019 | 632 | 2019 |
| Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning B Gunel, J Du, A Conneau, V Stoyanov ICLR 2021, 2020 | 518 | 2020 |
| Product embeddings using side-information for recommendation F Vasile, E Smirnova, A Conneau WWW 2016, 2016 | 324* | 2016 |
| Unsupervised Speech Recognition A Baevski, WN Hsu, A Conneau, M Auli NeurIPS 2021 (Oral), 2021 | 315 | 2021 |
| Emerging Cross-lingual Structure in Pretrained Language Models A Conneau, S Wu, H Li, L Zettlemoyer, V Stoyanov ACL 2020, 2019 | 266* | 2019 |
| Scaling speech technology to 1,000+ languages V Pratap, A Tjandra, B Shi, P Tomasello, A Babu, S Kundu, A Elkahky, ... JMLR 2024, 2023 | 254 | 2023 |
| FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech A Conneau, M Ma, S Khanuja, Y Zhang, V Axelrod, S Dalmia, J Riesa, ... SLT 2022 (Best Paper Award), 2022 | 242 | 2022 |
| Self-training and pre-training are complementary for speech recognition Q Xu, A Baevski, T Likhomanenko, P Tomasello, A Conneau, R Collobert, ... ICASSP 2021, 2020 | 191 | 2020 |
Guillaume LampleMistral AI在 mistral.ai 的电子邮件经过验证
