UniBO @ AMI: A Multi-Class Approach to Misogyny and Aggressiveness Identification on Twitter Posts Using AlBERTo
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11 mai 2021
- doi: 10.4000/books.aaccademia.6769
- eisbn: 9791280136329
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
Mots-clés
EVALITA COVID-19 Infodemic linguistica computazionale Automatic Misogyny Identification Misogyny on Twitter Posts AlBERTo Convolutional Neural Network BERT Model Hate Speech Detection Multimodal Meme Detection MEME management Language Game ``La Ghigliottina'' Language & Linguistics LAN000000 CBXSujets proches
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Arianna Muti et al., « UniBO @ AMI: A Multi-Class Approach to Misogyny and Aggressiveness Identification on Twitter Posts Using AlBERTo », Accademia University Press, ID : 10.4000/books.aaccademia.6769
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Résumé
We describe our participation in the EVALITA 2020 (Basile et al., 2020) shared task on Automatic Misogyny Identification. We focus on task A —Misogyny and Aggressive Behaviour Identification— which aims at detecting whether a tweet in Italian is misogynous and, if so, whether it is aggressive. Rather than building two different models, one for misogyny and one for aggressiveness identification, we handle the problem as one single multi-label classification task, considering three classes: non-misogynous, non-aggressive misogynous, and aggressive misogynous. Our three-class supervised model, built on top of AlBERTo, obtains an overall F1 score of 0.7438 on the task test set (F1 = 0.8102 for the misogyny and F1 = 0.6774 for the aggressiveness task), which outperforms the top submitted model (F1 = 0.7406).