| 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 | 2562 | 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 | 1031 | 2024 |
| TaPas: Weakly supervised table parsing via pre-training J Herzig, PK Nowak, T Müller, F Piccinno, JM Eisenschlos arXiv preprint arXiv:2004.02349, 2020 | 662 | 2020 |
| GERBIL: general entity annotator benchmarking framework R Usbeck, M Röder, AC Ngonga Ngomo, C Baron, A Both, M Brümmer, ... Proceedings of the 24th international conference on World Wide Web, 1133-1143, 2015 | 294 | 2015 |
| From TagME to WAT: a new entity annotator F Piccinno, P Ferragina Proceedings of the first international workshop on Entity recognition …, 2014 | 250 | 2014 |
| Deplot: One-shot visual language reasoning by plot-to-table translation F Liu, JM Eisenschlos, F Piccinno, S Krichene, C Pang, K Lee, M Joshi, ... arXiv preprint arXiv:2212.10505, 2022 | 90 | 2022 |
| Matcha: Enhancing visual language pretraining with math reasoning and chart derendering F Liu, F Piccinno, S Krichene, C Pang, K Lee, M Joshi, Y Altun, N Collier, ... arXiv preprint arXiv:2212.09662, 2022 | 78 | 2022 |
| On analyzing hashtags in twitter P Ferragina, F Piccinno, R Santoro Proceedings of the international AAAI conference on web and social media 9 …, 2015 | 65 | 2015 |
| Answering conversational questions on structured data without logical forms T Mueller, F Piccinno, M Nicosia, P Shaw, Y Altun arXiv preprint arXiv:1908.11787, 2019 | 44 | 2019 |
| Generating logical forms from graph representations of text and entities P Shaw, P Massey, A Chen, F Piccinno, Y Altun arXiv preprint arXiv:1905.08407, 2019 | 43 | 2019 |
| Structured context and high-coverage grammar for conversational question answering over knowledge graphs P Marion, PK Nowak, F Piccinno arXiv preprint arXiv:2109.00269, 2021 | 37 | 2021 |
| Swat: A system for detecting salient Wikipedia entities in texts M Ponza, P Ferragina, F Piccinno Computational Intelligence 35 (4), 858-890, 2019 | 33 | 2019 |
| Table-to-text generation and pre-training with tabt5 E Andrejczuk, JM Eisenschlos, F Piccinno, S Krichene, Y Altun arXiv preprint arXiv:2210.09162, 2022 | 31 | 2022 |
| Revisiting taxonomy induction over wikipedia A Gupta, F Piccinno, M Kozhevnikov, M Pasca, D Pighin Proceedings of COLING 2016, the 26th International Conference on …, 2016 | 31 | 2016 |
| mmt5: Modular multilingual pre-training solves source language hallucinations J Pfeiffer, F Piccinno, M Nicosia, X Wang, M Reid, S Ruder arXiv preprint arXiv:2305.14224, 2023 | 18 | 2023 |
| Compressed indexes for string searching in labeled graphs P Ferragina, F Piccinno, R Venturini Proceedings of the 24th International Conference on World Wide Web, 322-332, 2015 | 9 | 2015 |
| Document aboutness via sophisticated syntactic and semantic features M Ponza, P Ferragina, F Piccinno Natural Language Processing and Information Systems: 22nd International …, 2017 | 8 | 2017 |
| Algorithms and data structures for big labeled graphs F Piccinno Università degli Studi di Pisa, 2017 | 5 | 2017 |
| Evaluating byte and wordpiece level models for massively multilingual semantic parsing M Nicosia, F Piccinno arXiv preprint arXiv:2212.07223, 2022 | 4 | 2022 |
| What Did You Say? Task-Oriented Dialog Datasets Are Not Conversational!? AS Jakobovits, F Piccinno, Y Altun arXiv preprint arXiv:2203.03431, 2022 | 4 | 2022 |
Julian Martin EisenschlosNLP Researcher, Google DeepMindEmail verificata su google.com