| 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 | 1611 | 2023 |
| Learning to rank short text pairs with convolutional deep neural networks A Severyn, A Moschitti Proceedings of the 38th international ACM SIGIR conference on research and …, 2015 | 911 | 2015 |
| Twitter sentiment analysis with deep convolutional neural networks A Severyn, A Moschitti Proceedings of the 38th international ACM SIGIR conference on research and …, 2015 | 860 | 2015 |
| Globally normalized transition-based neural networks D Andor, C Alberti, D Weiss, A Severyn, A Presta, K Ganchev, S Petrov, ... ACL, 2016 | 720 | 2016 |
| Leveraging pre-trained checkpoints for sequence generation tasks S Rothe, S Narayan, A Severyn ACL, 2019 | 481 | 2019 |
| Neural ranking models with weak supervision M Dehghani, H Zamani, A Severyn, J Kamps, WB Croft Proceedings of the 40th international ACM SIGIR conference on research and …, 2017 | 423 | 2017 |
| 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 | 412 | 2024 |
| A Hybrid Convolutional Variational Autoencoder for Text Generation S Semeniuta, A Severyn, E Barth EMNLP, 2017 | 305 | 2017 |
| UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification A Severyn, A Moschitti Semeval, 2015 | 293 | 2015 |
| Recurrent dropout without memory loss S Semeniuta, A Severyn, E Barth COLING, 2016 | 289 | 2016 |
| Encode, Tag, Realize: High-Precision Text Editing E Malmi, S Krause, S Rothe, D Mirylenka, A Severyn EMNLP, 2019 | 208 | 2019 |
| Teaching small language models to reason LC Magister, J Mallinson, J Adamek, E Malmi, A Severyn arXiv preprint arXiv:2212.08410, 2022 | 176 | 2022 |
| Automatic Feature Engineering for Answer Selection and Extraction A Severyn, A Moschitti EMNLP, 2013 | 169 | 2013 |
| Leveraging large amounts of weakly supervised data for multi-language sentiment classification J Deriu, A Lucchi, V De Luca, A Severyn, S Müller, M Cieliebak, ... Proceedings of the 26th international conference on world wide web, 1045-1052, 2017 | 159 | 2017 |
| A simple recipe for multilingual grammatical error correction S Rothe, J Mallinson, E Malmi, S Krause, A Severyn arXiv preprint arXiv:2106.03830, 2021 | 150 | 2021 |
| Multi-lingual opinion mining on YouTube A Severyn, A Moschitti, O Uryupina, B Plank, K Filippova Information Processing & Management 52 (1), 46-60, 2016 | 120 | 2016 |
| Structural relationships for large-scale learning of answer re-ranking A Severyn, A Moschitti Proceedings of the 35th international ACM SIGIR conference on Research and …, 2012 | 95 | 2012 |
| On accurate evaluation of gans for language generation S Semeniuta, A Severyn, S Gelly arXiv preprint arXiv:1806.04936, 2018 | 94 | 2018 |
| Unsupervised text style transfer with padded masked language models E Malmi, A Severyn, S Rothe arXiv preprint arXiv:2010.01054, 2020 | 80 | 2020 |
| SenTube: a corpus for sentiment analysis on YouTube social media O Uryupina, B Plank, A Severyn, A Rotondi, A Moschitti LREC, 2014 | 78 | 2014 |
Alessandro MoschittiPrincipal Scientist at Amazon AlexaVerified email at amazon.com
