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Tommaso Fornaciari
Tommaso Fornaciari
Verified email at unibocconi.it
Title
Cited by
Cited by
Year
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
1212021
Automatic deception detection in Italian court cases
T Fornaciari, M Poesio
Artificial intelligence and law 21, 303-340, 2013
1022013
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
992021
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
872021
Automatic detection of verbal deception
E Fitzpatrick, J Bachenko, T Fornaciari
Springer Nature, 2022
832022
“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
76*2020
Identifying fake Amazon reviews as learning from crowds
T Fornaciari, M Poesio
Proceedings of the 14th Conference of the European Chapter of the …, 2014
762014
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
432020
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
392021
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
292013
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
262023
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
212020
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
172021
Lexical vs. surface features in deceptive language analysis
T Fornaciari, M Poesio
Proceedings of the ICAIL 2011 Workshop: Applying Human Language Technology …, 2011
172011
DeCour: a corpus of DEceptive statements in Italian COURts.
T Fornaciari, M Poesio
LREC, 1585-1590, 2012
162012
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
152012
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
13*2018
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
122019
Peer networks and entrepreneurship: A Pan-African RCT
F Vega-Redondo, P Pin, D Ubfal, C Benedetti-Fasil, C Brummitt, ...
IZA Discussion Paper, 2019
112019
Detecting Deception in Italian Criminal Proceedings
T Fornaciari, M Poesio
Proceedings of the 10th Biennial Conference of the International Association …, 2011
92011
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