| Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors M Baroni, G Dinu, G Kruszewski Proceedings of Association for Computational Linguistics (ACL) 1, 2014 | 2124 | 2014 |
| Improving zero-shot learning by mitigating the hubness problem G Dinu, A Lazaridou, M Baroni arXiv preprint arXiv:1412.6568, 2014 | 460 | 2014 |
| Hubness and pollution: Delving into cross-space mapping for zero-shot learning A Lazaridou, G Dinu, M Baroni Zong C, Strube M, editors. Proceedings of the 53rd Annual Meeting of the …, 2015 | 265 | 2015 |
| Training neural machine translation to apply terminology constraints G Dinu, P Mathur, M Federico, Y Al-Onaizan arXiv preprint arXiv:1906.01105, 2019 | 184 | 2019 |
| Learning morphology with morfette G Chrupała, G Dinu, J Van Genabith International Conference on Language Resources and Evaluation (LREC), 2008 | 168 | 2008 |
| Multi-step regression learning for compositional distributional semantics E Grefenstette, G Dinu, YZ Zhang, M Sadrzadeh, M Baroni International Conference on Computational Semantics (IWCS), 2013 | 155 | 2013 |
| Measuring distributional similarity in context G Dinu, M Lapata Proceedings of Empirical Methods in Natural Language Processing (EMNLP …, 2010 | 153 | 2010 |
| Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection J Ni, G Dinu, R Florian ACL, 2017 | 151 | 2017 |
| Findings of the IWSLT 2022 Evaluation Campaign. A Anastasopoulos, L Barrault, L Bentivogli, MZ Boito, O Bojar, R Cattoni, ... Proceedings of the 19th International Conference on Spoken Language …, 2022 | 109 | 2022 |
| DISSECT: DIStributional SEmantics Composition Toolkit G Dinu, NT Pham, M Baroni Proceedings of the System Demonstrations of ACL, 2013 | 106 | 2013 |
| Evaluating robustness to input perturbations for neural machine translation X Niu, P Mathur, G Dinu, Y Al-Onaizan arXiv preprint arXiv:2005.00580, 2020 | 65 | 2020 |
| Toward mention detection robustness with recurrent neural networks TH Nguyen, A Sil, G Dinu, R Florian arXiv preprint arXiv:1602.07749, 2016 | 58 | 2016 |
| How to make words with vectors: Phrase generation in distributional semantics G Dinu, M Baroni Proceedings of the Association for Computational Linguistics (ACL 2014), 2014 | 47 | 2014 |
| General estimation and evaluation of compositional distributional semantic models G Dinu, NT Pham, M Baroni ACL 2013 Workshop on Continuous Vector Space Models and their …, 2013 | 42* | 2013 |
| Ranking paraphrases in context S Thater, G Dinu, M Pinkal ACL 2009 Workshop on Applied Textual Inference, 44-47, 2009 | 41 | 2009 |
| Inference rules and their application to recognizing textual entailment G Dinu, R Wang European Chapter of the Association for Computational Linguistics (EACL …, 2009 | 41 | 2009 |
| Nghia The Pham, and Marco Baroni. General estimation and evaluation of compositional distributional semantic models G Dinu Workshop on Continuous Vector Space Models and their Compositionality, Sofia …, 2013 | 33* | 2013 |
| Distilling multiple domains for neural machine translation A Currey, P Mathur, G Dinu Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 29 | 2020 |
| A relatedness benchmark to test the role of determiners in compositional distributional semantics. R Bernardi, G Dinu, M Marelli, M Baroni Proceedings of Association for Computational Linguistics ACL (2), 53-57, 2013 | 29 | 2013 |
| MT-GenEval: A counterfactual and contextual dataset for evaluating gender accuracy in machine translation A Currey, M Nădejde, R Pappagari, M Mayer, S Lauly, X Niu, B Hsu, ... arXiv preprint arXiv:2211.01355, 2022 | 28 | 2022 |
Marco BaroniICREA Professor (Barcelona)確認したメール アドレス: upf.edu
