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.
Rizzi, G., Astorino, A., Scalena, D., Rosso, P., Fersini, E. (2023). MIND at SemEval-2023 Task 11: From Uncertain Predictions to Subjective Disagreement. In 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop (pp.556-564). Association for Computational Linguistics.
MIND at SemEval-2023 Task 11: From Uncertain Predictions to Subjective Disagreement
Rizzi G.;Astorino A.;Scalena D.;Fersini E.
2023
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.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
