@inproceedings{ravagli-vaiani-2022-jrlv,
title = "{JRLV} at {S}em{E}val-2022 Task 5: The Importance of Visual Elements for Misogyny Identification in Memes",
author = "Ravagli, Jason and
Vaiani, Lorenzo",
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-1.84/",
doi = "10.18653/v1/2022.semeval-1.84",
pages = "610--617",
abstract = "Gender discrimination is a serious and widespread problem on social media and online in general. Besides offensive messages, memes are one of the main means of dissemination for such content. With these premises, the MAMI task was proposed at the SemEval-2022, which consists of identifying memes with misogynous characteristics. In this work, we propose a solution to this problem based on Mask R-CNN and VisualBERT that leverages the multimodal nature of the task. Our study focuses on observing how the two sources of data in memes (text and image) and their possible combinations impact performances. Our best result slightly exceeds the higher baseline, but the experiments allowed us to draw important considerations regarding the importance of correctly exploiting the visual information and the relevance of the elements present in the memes images."
}
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<abstract>Gender discrimination is a serious and widespread problem on social media and online in general. Besides offensive messages, memes are one of the main means of dissemination for such content. With these premises, the MAMI task was proposed at the SemEval-2022, which consists of identifying memes with misogynous characteristics. In this work, we propose a solution to this problem based on Mask R-CNN and VisualBERT that leverages the multimodal nature of the task. Our study focuses on observing how the two sources of data in memes (text and image) and their possible combinations impact performances. Our best result slightly exceeds the higher baseline, but the experiments allowed us to draw important considerations regarding the importance of correctly exploiting the visual information and the relevance of the elements present in the memes images.</abstract>
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%0 Conference Proceedings
%T JRLV at SemEval-2022 Task 5: The Importance of Visual Elements for Misogyny Identification in Memes
%A Ravagli, Jason
%A Vaiani, Lorenzo
%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 ravagli-vaiani-2022-jrlv
%X Gender discrimination is a serious and widespread problem on social media and online in general. Besides offensive messages, memes are one of the main means of dissemination for such content. With these premises, the MAMI task was proposed at the SemEval-2022, which consists of identifying memes with misogynous characteristics. In this work, we propose a solution to this problem based on Mask R-CNN and VisualBERT that leverages the multimodal nature of the task. Our study focuses on observing how the two sources of data in memes (text and image) and their possible combinations impact performances. Our best result slightly exceeds the higher baseline, but the experiments allowed us to draw important considerations regarding the importance of correctly exploiting the visual information and the relevance of the elements present in the memes images.
%R 10.18653/v1/2022.semeval-1.84
%U https://aclanthology.org/2022.semeval-1.84/
%U https://doi.org/10.18653/v1/2022.semeval-1.84
%P 610-617
Markdown (Informal)
[JRLV at SemEval-2022 Task 5: The Importance of Visual Elements for Misogyny Identification in Memes](https://aclanthology.org/2022.semeval-1.84/) (Ravagli & Vaiani, SemEval 2022)
ACL