Google's neural machine translation system: Bridging the gap between human and machine translation Y Wu arXiv preprint arXiv:1609.08144, 2016 | 9131 | 2016 |
Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2192 | 2023 |
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 689 | 2024 |
Google usm: Scaling automatic speech recognition beyond 100 languages Y Zhang, W Han, J Qin, Y Wang, A Bapna, Z Chen, N Chen, B Li, ... arXiv preprint arXiv:2303.01037, 2023 | 261 | 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, ... 2022 IEEE Spoken Language Technology Workshop (SLT), 798-805, 2023 | 242 | 2023 |
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 | 154 | 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 | 112 | 2022 |
Google’s neural machine translation system: Bridging the gap between human and machine translation. CoRR abs/1609.08144 (2016) Y Wu, M Schuster, Z Chen, QV Le, M Norouzi, W Macherey, M Krikun, ... arXiv preprint arXiv:1609.08144, 2016 | 104 | 2016 |
SLAM: A unified encoder for speech and language modeling via speech-text joint pre-training A Bapna, Y Chung, N Wu, A Gulati, Y Jia, JH Clark, M Johnson, J Riesa, ... arXiv preprint arXiv:2110.10329, 2021 | 91 | 2021 |
Google’s neural machine translation system: Bridging the gap between human and machine translation. CoRR abs/1609.08144 Y Wu, M Schuster, Z Chen, QV Le, M Norouzi, W Macherey, M Krikun, ... | 82 | 2016 |
Building machine translation systems for the next thousand languages A Bapna, I Caswell, J Kreutzer, O Firat, D van Esch, A Siddhant, M Niu, ... arXiv preprint arXiv:2205.03983, 2022 | 81 | 2022 |
Evaluating the cross-lingual effectiveness of massively multilingual neural machine translation A Siddhant, M Johnson, H Tsai, N Ari, J Riesa, A Bapna, O Firat, K Raman Proceedings of the AAAI conference on artificial intelligence 34 (05), 8854-8861, 2020 | 71 | 2020 |
Improving multilingual models with language-clustered vocabularies HW Chung, D Garrette, KC Tan, J Riesa arXiv preprint arXiv:2010.12777, 2020 | 61 | 2020 |
Hierarchical search for word alignment J Riesa, D Marcu Proceedings of the 48th annual meeting of the association for computational …, 2010 | 51 | 2010 |
A fast, compact, accurate model for language identification of codemixed text Y Zhang, J Riesa, D Gillick, A Bakalov, J Baldridge, D Weiss arXiv preprint arXiv:1810.04142, 2018 | 40 | 2018 |
Minimally supervised morphological segmentation with applications to machine translation J Riesa, D Yarowsky Proceedings of the 7th Conference of the Association for Machine Translation …, 2006 | 25 | 2006 |
Finding fast transformers: One-shot neural architecture search by component composition H Tsai, J Ooi, CS Ferng, HW Chung, J Riesa arXiv preprint arXiv:2008.06808, 2020 | 24 | 2020 |
Building an English-Iraqi Arabic machine translation system for spoken utterances with limited resources J Riesa, B Mohit, K Knight, D Marcu Ninth International Conference on Spoken Language Processing, 2006 | 22 | 2006 |
Xtreme-s: Evaluating cross-lingual speech representations A Conneau, A Bapna, Y Zhang, M Ma, P von Platen, A Lozhkov, C Cherry, ... arXiv preprint arXiv:2203.10752, 2022 | 21 | 2022 |
SQuId: Measuring speech naturalness in many languages T Sellam, A Bapna, J Camp, D Mackinnon, AP Parikh, J Riesa ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 19 | 2023 |