| RoBERTa: A Robustly Optimized BERT Pretraining Approach Y Liu, M Ott, N Goyal, J Du, M Joshi, D Chen, O Levy, M Lewis, ... arXiv preprint arXiv:1907.11692, 2019 | 31944* | 2019 |
| Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension M Lewis, Y Liu, N Goyal, M Ghazvininejad, A Mohamed, O Levy, ... arXiv preprint arXiv:1910.13461, 2019 | 12180 | 2019 |
| Unsupervised Cross-lingual Representation Learning at Scale A Conneau, K Khandelwal, N Goyal, V Chaudhary, G Wenzek, F Guzmán, ... arXiv preprint arXiv:1911.02116, 2019 | 7062 | 2019 |
| SemEval-2017 task 4: Sentiment analysis in Twitter S Rosenthal, N Farra, P Nakov Proceedings of the 11th International Workshop on Semantic Evaluation …, 2017 | 1891 | 2017 |
| XNLI: Evaluating Cross-lingual Sentence Representations A Conneau, G Lample, R Rinott, A Williams, SR Bowman, H Schwenk, ... arXiv preprint arXiv:1809.05053, 2018 | 1488 | 2018 |
| SemEval-2016 task 4: Sentiment analysis in Twitter P Nakov, A Ritter, S Rosenthal, F Sebastiani, V Stoyanov Proceedings of the 10th International Workshop on Semantic Evaluation …, 2016 | 1386 | 2016 |
| Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning B Gunel, J Du, A Conneau, V Stoyanov arXiv preprint arXiv:2011.01403, 2020 | 530 | 2020 |
| System and method for automatically summarizing fine-grained opinions in digital text C Cardie, V Stoyanov, Y Choi, E Breck US Patent 8,280,885, 2012 | 461 | 2012 |
| RoBERTa: A Robustly Optimized BERT Pretraining Approach. July 2019 Y Liu, M Ott, N Goyal, J Du, M Joshi, D Chen, O Levy, M Lewis, ... arXiv preprint arXiv:1907.11692, 1907 | 328* | 1907 |
| Emerging Cross-lingual Structure in Pretrained Language Models A Conneau, S Wu, H Li, L Zettlemoyer, V Stoyanov Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 286* | 2020 |
| Conundrums in noun phrase coreference resolution: Making sense of the state-of-the-art V Stoyanov, N Gilbert, C Cardie, E Riloff Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL …, 2009 | 211 | 2009 |
| Lever: Learning to verify language-to-code generation with execution A Ni, S Iyer, D Radev, V Stoyanov, W Yih, S Wang, XV Lin International Conference on Machine Learning, 26106-26128, 2023 | 207 | 2023 |
| Topic identification for fine-grained opinion analysis V Stoyanov, C Cardie Proceedings of the 22nd International Conference on Computational …, 2008 | 196 | 2008 |
| Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art P Lewis, M Ott, J Du, V Stoyanov Proceedings of the 3rd Clinical Natural Language Processing Workshop, 146-157, 2020 | 192 | 2020 |
| Multi-perspective question answering using the OpQA corpus V Stoyanov, C Cardie, J Wiebe Proceedings of Human Language Technology Conference and Conference on …, 2005 | 182 | 2005 |
| Self-training improves pre-training for natural language understanding J Du, E Grave, B Gunel, V Chaudhary, O Celebi, M Auli, V Stoyanov, ... arXiv preprint arXiv:2010.02194, 2020 | 180 | 2020 |
| Empirical risk minimization of graphical model parameters given approximate inference, decoding, and model structure V Stoyanov, A Ropson, J Eisner Proceedings of the Fourteenth International Conference on Artificial …, 2011 | 160 | 2011 |
| Efficient Large Scale Language Modeling with Mixtures of Experts M Artetxe, S Bhosale, N Goyal, T Mihaylov, M Ott, S Shleifer, XV Lin, J Du, ... arXiv preprint arXiv:2112.10684, 2021 | 144 | 2021 |
| Few-shot Learning with Multilingual Language Models XV Lin, T Mihaylov, M Artetxe, T Wang, S Chen, D Simig, M Ott, N Goyal, ... arXiv preprint arXiv:2112.10668, 2021 | 141* | 2021 |
| A multi-lingual multi-task architecture for low-resource sequence labeling Y Lin, S Yang, V Stoyanov, H Ji Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 132 | 2018 |
Myle OttGoogle DeepMindVerified email at google.com
