| Google’s neural machine translation system: Bridging the gap between human and machine translation. Y Wu, M Schuster, Z Chen, QV Le, M Norouzi, W Macherey, M Krikun, ... arXiv preprint arXiv:1609.08144, 2016 | 8972 | 2016 |
| Google’s multilingual neural machine translation system: Enabling zero-shot translation M Johnson, M Schuster, QV Le, M Krikun, Y Wu, Z Chen, N Thorat, ... Transactions of the Association for Computational Linguistics 5, 339-351, 2017 | 2365 | 2017 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1396 | 2023 |
| Palm 2 technical report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 1191 | 2023 |
| Leveraging linguistic structure for open domain information extraction G Angeli, MJJ Premkumar, CD Manning Proceedings of the 53rd Annual Meeting of the Association for Computational …, 2015 | 939 | 2015 |
| Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation J Hu, S Ruder, A Siddhant, G Neubig, O Firat, M Johnson International Conference on Machine Learning, 4411-4421, 2020 | 883 | 2020 |
| Massively multilingual neural machine translation R Aharoni, M Johnson, O Firat arXiv preprint arXiv:1903.00089, 2019 | 578 | 2019 |
| The best of both worlds: Combining recent advances in neural machine translation MX Chen, O Firat, A Bapna, M Johnson, W Macherey, G Foster, L Jones, ... arXiv preprint arXiv:1804.09849, 2018 | 532 | 2018 |
| Massively multilingual neural machine translation in the wild: Findings and challenges N Arivazhagan, A Bapna, O Firat, D Lepikhin, M Johnson, M Krikun, ... arXiv preprint arXiv:1907.05019, 2019 | 402 | 2019 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 311 | 2024 |
| Direct speech-to-speech translation with a sequence-to-sequence model Y Jia, RJ Weiss, F Biadsy, W Macherey, M Johnson, Z Chen, Y Wu arXiv preprint arXiv:1904.06037, 2019 | 233 | 2019 |
| Lingvo: a modular and scalable framework for sequence-to-sequence modeling J Shen, P Nguyen, Y Wu, Z Chen, MX Chen, Y Jia, A Kannan, T Sainath, ... arXiv preprint arXiv:1902.08295, 2019 | 209 | 2019 |
| Machine learning in automatic speech recognition: A survey J Padmanabhan, MJ Johnson Premkumar IETE Technical Review 32 (4), 240-251, 2015 | 205 | 2015 |
| Leveraging weakly supervised data to improve end-to-end speech-to-text translation Y Jia, M Johnson, W Macherey, RJ Weiss, Y Cao, CC Chiu, N Ari, ... ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 175 | 2019 |
| Small and practical BERT models for sequence labeling H Tsai, J Riesa, M Johnson, N Arivazhagan, X Li, A Archer arXiv preprint arXiv:1909.00100, 2019 | 151 | 2019 |
| XTREME-R: Towards more challenging and nuanced multilingual evaluation S Ruder, N Constant, J Botha, A Siddhant, O Firat, J Fu, P Liu, J Hu, ... arXiv preprint arXiv:2104.07412, 2021 | 136 | 2021 |
| Gemini: A family of highly capable multimodal models R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805 1, 2023 | 132 | 2023 |
| Rethinking embedding coupling in pre-trained language models HW Chung, T Fevry, H Tsai, M Johnson, S Ruder arXiv preprint arXiv:2010.12821, 2020 | 131 | 2020 |
| The missing ingredient in zero-shot neural machine translation N Arivazhagan, A Bapna, O Firat, R Aharoni, M Johnson, W Macherey arXiv preprint arXiv:1903.07091, 2019 | 110 | 2019 |
| mslam: Massively multilingual joint pre-training for speech and text A Bapna, C Cherry, Y Zhang, Y Jia, M Johnson, Y Cheng, S Khanuja, ... arXiv preprint arXiv:2202.01374, 2022 | 108 | 2022 |
Yonghui WuGoogle BrainVerified email at google.com
