@inproceedings{rizzi-etal-2023-mind,
title = "{MIND} at {S}em{E}val-2023 Task 11: From Uncertain Predictions to Subjective Disagreement",
author = "Rizzi, Giulia and
Astorino, Alessandro and
Scalena, Daniel and
Rosso, Paolo and
Fersini, Elisabetta",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.77",
doi = "10.18653/v1/2023.semeval-1.77",
pages = "556--564",
abstract = "This paper describes the participation of the research laboratory MIND, at the University of Milano-Bicocca, in the SemEval 2023 task related to Learning With Disagreements (Le-Wi-Di). The main goal is to identify the level of agreement/disagreement from a collection of textual datasets with different characteristics in terms of style, language and task. The proposed approach is grounded on the hypothesis that the disagreement between annotators could be grasped by the uncertainty that a model, based on several linguistic characteristics, could have on the prediction of a given gold label.",
}
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%0 Conference Proceedings
%T MIND at SemEval-2023 Task 11: From Uncertain Predictions to Subjective Disagreement
%A Rizzi, Giulia
%A Astorino, Alessandro
%A Scalena, Daniel
%A Rosso, Paolo
%A Fersini, Elisabetta
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F rizzi-etal-2023-mind
%X This paper describes the participation of the research laboratory MIND, at the University of Milano-Bicocca, in the SemEval 2023 task related to Learning With Disagreements (Le-Wi-Di). The main goal is to identify the level of agreement/disagreement from a collection of textual datasets with different characteristics in terms of style, language and task. The proposed approach is grounded on the hypothesis that the disagreement between annotators could be grasped by the uncertainty that a model, based on several linguistic characteristics, could have on the prediction of a given gold label.
%R 10.18653/v1/2023.semeval-1.77
%U https://aclanthology.org/2023.semeval-1.77
%U https://doi.org/10.18653/v1/2023.semeval-1.77
%P 556-564
Markdown (Informal)
[MIND at SemEval-2023 Task 11: From Uncertain Predictions to Subjective Disagreement](https://aclanthology.org/2023.semeval-1.77) (Rizzi et al., SemEval 2023)
ACL