I2C at SemEval-2022 Task 5: Identification of misogyny in internet memes

Pablo Cordon, Pablo Gonzalez Diaz, Jacinto Mata, Victoria Pachón


Abstract
In this paper we present our approach and system description on Task 5 A in MAMI: Multimedia Automatic Misogyny Identification. In our experiments we compared several architectures based on deep learning algorithms with various other approaches to binary classification using Transformers, combined with a nudity image detection algorithm to provide better results. With this approach, we achieved an F1-score of 0.665 in the evaluation process
Anthology ID:
2022.semeval-1.94
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
689–694
Language:
URL:
https://aclanthology.org/2022.semeval-1.94
DOI:
10.18653/v1/2022.semeval-1.94
Bibkey:
Cite (ACL):
Pablo Cordon, Pablo Gonzalez Diaz, Jacinto Mata, and Victoria Pachón. 2022. I2C at SemEval-2022 Task 5: Identification of misogyny in internet memes. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 689–694, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
I2C at SemEval-2022 Task 5: Identification of misogyny in internet memes (Cordon et al., SemEval 2022)
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PDF:
https://aclanthology.org/2022.semeval-1.94.pdf