| Learning from disagreement: A survey AN Uma, T Fornaciari, D Hovy, S Paun, B Plank, M Poesio Journal of Artificial Intelligence Research 72, 1385-1470, 2021 | 185 | 2021 |
| We need to consider disagreement in evaluation V Basile, M Fell, T Fornaciari, D Hovy, S Paun, B Plank, M Poesio, A Uma Proceedings of the 1st workshop on benchmarking: past, present and future, 15-21, 2021 | 121 | 2021 |
| Beyond black & white: Leveraging annotator disagreement via soft-label multi-task learning T Fornaciari, A Uma, S Paun, B Plank, D Hovy, M Poesio Proceedings of the 2021 Conference of the North American Chapter of the …, 2021 | 116 | 2021 |
| Automatic deception detection in Italian court cases T Fornaciari, M Poesio Artificial intelligence and law 21, 303-340, 2013 | 113 | 2013 |
| “you sound just like your father” commercial machine translation systems include stylistic biases D Hovy, F Bianchi, T Fornaciari Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 88* | 2020 |
| Automatic detection of verbal deception E Fitzpatrick, J Bachenko, T Fornaciari Morgan & Claypool Publishers, 2015 | 86 | 2015 |
| Identifying fake amazon reviews as learning from crowds T Fornaciari, M Poesio Proceedings of the 14th Conference of the European Chapter of the …, 2014 | 83 | 2014 |
| SemEval-2021 task 12: Learning with disagreements A Uma, T Fornaciari, A Dumitrache, T Miller, J Chamberlain, B Plank, ... Proceedings of the 15th International Workshop on Semantic Evaluation …, 2021 | 61 | 2021 |
| A case for soft loss functions A Uma, T Fornaciari, D Hovy, S Paun, B Plank, M Poesio Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 8 …, 2020 | 56 | 2020 |
| SemEval-2023 task 11: Learning with disagreements (LeWiDi) E Leonardelli, A Uma, G Abercrombie, D Almanea, V Basile, T Fornaciari, ... arXiv preprint arXiv:2304.14803, 2023 | 45 | 2023 |
| The effect of personality type on deceptive communication style T Fornaciari, F Celli, M Poesio 2013 European Intelligence and Security Informatics Conference, 1-6, 2013 | 27 | 2013 |
| Fake opinion detection: how similar are crowdsourced datasets to real data? T Fornaciari, L Cagnina, P Rosso, M Poesio Language Resources and Evaluation 54, 1019-1058, 2020 | 24 | 2020 |
| BERTective: Language models and contextual information for deception detection T Fornaciari, F Bianchi, M Poesio, D Hovy Proceedings of the 16th Conference of the European Chapter of the …, 2021 | 22 | 2021 |
| DeCour: a corpus of DEceptive statements in Italian COURts. T Fornaciari, M Poesio LREC, 1585-1590, 2012 | 17 | 2012 |
| Lexical vs. surface features in deceptive language analysis T Fornaciari, M Poesio Proceedings of the ICAIL 2011 Workshop: Applying Human Language Technology …, 2011 | 17 | 2011 |
| On the use of homogenous sets of subjects in deceptive language analysis T Fornaciari, M Poesio Proceedings of the Workshop on Computational Approaches to Deception …, 2012 | 16 | 2012 |
| Geolocation with attention-based multitask learning models T Fornaciari, D Hovy Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019 …, 2019 | 14 | 2019 |
| Increasing in-class similarity by retrofitting embeddings with demographic information D Hovy, T Fornaciari Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 14* | 2018 |
| Peer networks and entrepreneurship: A Pan-African RCT F Vega-Redondo, P Pin, D Ubfal, C Benedetti-Fasil, C Brummitt, ... IZA Discussion Paper, 2019 | 13 | 2019 |
| Hard and soft evaluation of NLP models with BOOtSTrap SAmpling-BooStSa T Fornaciari, A Uma, M Poesio, D Hovy Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 9 | 2022 |
Dirk HovyBocconi UniversityEmail verificata su unibocconi.it
