Deep Learning Representations in Automatic Misogyny Identification: What Do We Gain and What Do We Miss?
Elisabetta Fersini, Luca Rosato, Antonio Candelieri, Francesco Archetti, Enza Messina · Accademia University Press eBooks · 2022
In this paper, we address the problem of automatic misogyny identification focusing on understanding the representation capabilities of widely adopted embeddings and addressing the problem of unintended bias. The proposed framework, grounded on Sentence Embeddings and Multi-Objective Bayesian Optimization, has been validated on an Italian dataset. We highlight capabilities and weaknesses related to the use of pre-trained language, as well as the contribution of Bayesian Optimization for mitigating the problem of biased predictions.