@inproceedings{lorentz-moreira-2022-inf,
title = "{INF}-{UFRGS} at {S}em{E}val-2022 Task 5: analyzing the performance of multimodal models",
author = "Lorentz, Gustavo and
Moreira, Viviane",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.95",
doi = "10.18653/v1/2022.semeval-1.95",
pages = "695--699",
abstract = "This paper describes INF-UFRGS submission for SemEval-2022 Task 5 Multimodal Automatic Misogyny Identification (MAMI). Unprecedented levels of harassment came with the ever-growing internet usage as a mean of worldwide communication. The goal of the task is to improve the quality of existing methods for misogyny identification, many of which require dedicated personnel, hence the need for automation. We experimented with five existing models, including ViLBERT and Visual BERT - both uni and multimodally pretrained - and MMBT. The datasets consist of memes with captions in English. The results show that all models achieved Macro-F1 scores above 0.64. ViLBERT was the best performer with a score of 0.698.",
}
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<abstract>This paper describes INF-UFRGS submission for SemEval-2022 Task 5 Multimodal Automatic Misogyny Identification (MAMI). Unprecedented levels of harassment came with the ever-growing internet usage as a mean of worldwide communication. The goal of the task is to improve the quality of existing methods for misogyny identification, many of which require dedicated personnel, hence the need for automation. We experimented with five existing models, including ViLBERT and Visual BERT - both uni and multimodally pretrained - and MMBT. The datasets consist of memes with captions in English. The results show that all models achieved Macro-F1 scores above 0.64. ViLBERT was the best performer with a score of 0.698.</abstract>
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%0 Conference Proceedings
%T INF-UFRGS at SemEval-2022 Task 5: analyzing the performance of multimodal models
%A Lorentz, Gustavo
%A Moreira, Viviane
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F lorentz-moreira-2022-inf
%X This paper describes INF-UFRGS submission for SemEval-2022 Task 5 Multimodal Automatic Misogyny Identification (MAMI). Unprecedented levels of harassment came with the ever-growing internet usage as a mean of worldwide communication. The goal of the task is to improve the quality of existing methods for misogyny identification, many of which require dedicated personnel, hence the need for automation. We experimented with five existing models, including ViLBERT and Visual BERT - both uni and multimodally pretrained - and MMBT. The datasets consist of memes with captions in English. The results show that all models achieved Macro-F1 scores above 0.64. ViLBERT was the best performer with a score of 0.698.
%R 10.18653/v1/2022.semeval-1.95
%U https://aclanthology.org/2022.semeval-1.95
%U https://doi.org/10.18653/v1/2022.semeval-1.95
%P 695-699
Markdown (Informal)
[INF-UFRGS at SemEval-2022 Task 5: analyzing the performance of multimodal models](https://aclanthology.org/2022.semeval-1.95) (Lorentz & Moreira, SemEval 2022)
ACL