| Probing for referential information in language models IT Sorodoc, K Gulordava, G Boleda Jurafsky D, Chai J, Schluter N, Tetreault J, editors. Proceedings of the …, 2020 | 38 | 2020 |
| “Look, some green circles!”: Learning to quantify from images I Sorodoc, A Lazaridou, G Boleda Torrent, AGG Herbelot, S Pezzelle, ... ACL 2016 5th Workshop on Vision and Language (VL’16): Proceedings of the …, 2016 | 22 | 2016 |
| Multimodal topic labelling I Sorodoc, JH Lau, N Aletras, T Baldwin Proceedings of the 15th Conference of the European Chapter of the …, 2017 | 17 | 2017 |
| What do entity-centric models learn? insights from entity linking in multi-party dialogue L Aina, C Silberer, M Westera, IT Sorodoc, G Boleda arXiv preprint arXiv:1905.06649, 2019 | 12 | 2019 |
| Comparatives, quantifiers, proportions: a multi-task model for the learning of quantities from vision S Pezzelle, IT Sorodoc, R Bernardi arXiv preprint arXiv:1804.05018, 2018 | 12 | 2018 |
| Aggregation methods for efficient collocation detection. A Dinu, LP Dinu, I Sorodoc LREC, 4041-4045, 2014 | 11 | 2014 |
| Learning quantification from images: A structured neural architecture I Sorodoc, S Pezzelle, A Herbelot, M Dimiccoli, R Bernardi Natural Language Engineering 24 (3), 363-392, 2018 | 9 | 2018 |
| AMORE-UPF at SemEval-2018 Task 4: BiLSTM with entity library L Aina, C Silberer, IT Sorodoc, M Westera, G Boleda arXiv preprint arXiv:1805.05370, 2018 | 7 | 2018 |
| Evaluating online continual learning with CALM G Kruszewski, IT Sorodoc, T Mikolov arXiv preprint arXiv:2004.03340, 2020 | 5 | 2020 |
| Class-agnostic continual learning of alternating languages and domains G Kruszewski, IT Sorodoc, T Mikolov arXiv preprint arXiv:2004.03340, 2020 | 3 | 2020 |
| Recurrent Instance Segmentation using Sequences of Referring Expressions A Herrera-Palacio, C Ventura, C Silberer, IT Sorodoc, G Boleda, ... arXiv preprint arXiv:1911.02103, 2019 | 3 | 2019 |
| Learning to identify and encode entities with deep learning models IT Sorodoc Universitat Pompeu Fabra, 2022 | | 2022 |
| Challenges in including extra-linguistic context in pre-trained language models IT Sorodoc, L Aina, G Boleda Tafreshi S, Sedoc J, Rogers A, Drozd A, Rumshisky A, Akula A, editors. The …, 2022 | | 2022 |
| Controlled tasks for model analysis: Retrieving discrete information from sequences I Sorodoc, G Boleda, M Baroni Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting …, 2021 | | 2021 |
| Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop I Sorodoc, M Sushil, E Takmaz, E Agirre Proceedings of the 16th Conference of the European Chapter of the …, 2021 | | 2021 |
| What do entity-centric models learn? Insights from entity linking in multi-party dialogue G Boleda, L Aina, C Silberer, IT Sorodoc, M Westera Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | | 2019 |
| Comparatives, quantifiers, proportions: a multi-task model for the learning of quantities from vision IT Sorodoc, S Pezzelle, R Bernardi Proceedings of the 2018 Conference of the North American Chapter of the …, 2018 | | 2018 |
| Pay attention to those sets! Learning quantification from images I Sorodoc, S Pezzelle, A Herbelot, M Dimiccoli, R Bernardi arXiv preprint arXiv:1704.02923, 2017 | | 2017 |
| Imparare a Quantificare Guardando (Learning to Quantify by Watching). S Pezzelle, I Sorodoc, A Herbelot, R Bernardi CLiC-it/EVALITA, 2016 | | 2016 |
| Learning to quantify from images IT Sorodoc UNIVERSITY OF MALTA, 2016 | | 2016 |
