@inproceedings{farinango-cuervo-parde-2022-exploring,
title = "Exploring Contrastive Learning for Multimodal Detection of Misogynistic Memes",
author = "Farinango Cuervo, Charic and
Parde, Natalie",
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-1.109",
doi = "10.18653/v1/2022.semeval-1.109",
pages = "785--792",
abstract = "Misogynistic memes are rampant on social media, and often convey their messages using multimodal signals (e.g., images paired with derogatory text or captions). However, to date very few multimodal systems have been leveraged for the detection of misogynistic memes. Recently, researchers have turned to contrastive learning, and most notably OpenAI{'}s CLIP model, is an innovative solution to a variety of multimodal tasks. In this work, we experiment with contrastive learning to address the detection of misogynistic memes within the context of SemEval 2022 Task 5. Although our model does not achieve top results, these experiments provide important exploratory findings for this task. We conduct a detailed error analysis, revealing promising clues and offering a foundation for follow-up work.",
}
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<abstract>Misogynistic memes are rampant on social media, and often convey their messages using multimodal signals (e.g., images paired with derogatory text or captions). However, to date very few multimodal systems have been leveraged for the detection of misogynistic memes. Recently, researchers have turned to contrastive learning, and most notably OpenAI’s CLIP model, is an innovative solution to a variety of multimodal tasks. In this work, we experiment with contrastive learning to address the detection of misogynistic memes within the context of SemEval 2022 Task 5. Although our model does not achieve top results, these experiments provide important exploratory findings for this task. We conduct a detailed error analysis, revealing promising clues and offering a foundation for follow-up work.</abstract>
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%0 Conference Proceedings
%T Exploring Contrastive Learning for Multimodal Detection of Misogynistic Memes
%A Farinango Cuervo, Charic
%A Parde, Natalie
%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 farinango-cuervo-parde-2022-exploring
%X Misogynistic memes are rampant on social media, and often convey their messages using multimodal signals (e.g., images paired with derogatory text or captions). However, to date very few multimodal systems have been leveraged for the detection of misogynistic memes. Recently, researchers have turned to contrastive learning, and most notably OpenAI’s CLIP model, is an innovative solution to a variety of multimodal tasks. In this work, we experiment with contrastive learning to address the detection of misogynistic memes within the context of SemEval 2022 Task 5. Although our model does not achieve top results, these experiments provide important exploratory findings for this task. We conduct a detailed error analysis, revealing promising clues and offering a foundation for follow-up work.
%R 10.18653/v1/2022.semeval-1.109
%U https://aclanthology.org/2022.semeval-1.109
%U https://doi.org/10.18653/v1/2022.semeval-1.109
%P 785-792
Markdown (Informal)
[Exploring Contrastive Learning for Multimodal Detection of Misogynistic Memes](https://aclanthology.org/2022.semeval-1.109) (Farinango Cuervo & Parde, SemEval 2022)
ACL