@inproceedings{mahadevan-etal-2022-transformers,
title = "Transformers at {S}em{E}val-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection",
author = "Mahadevan, Shankar and
Benhur, Sean and
Nayak, Roshan and
Subramanian, Malliga and
Shanmugavadivel, Kogilavani and
Sivanraju, Kanchana and
Chakravarthi, Bharathi Raja",
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-1.75",
doi = "10.18653/v1/2022.semeval-1.75",
pages = "550--554",
abstract = "Social media is an idea created to make theworld smaller and more connected. Recently,it has become a hub of fake news and sexistmemes that target women. Social Media shouldensure proper women{'}s safety and equality. Filteringsuch information from social media is ofparamount importance to achieving this goal. In this paper, we describe the system developedby our team for SemEval-2022 Task 5: MultimediaAutomatic Misogyny Identification. Wepropose a multimodal training methodologythat achieves good performance on both thesubtasks, ranking 4th for Subtask A (0.718macro F1-score) and 9th for Subtask B (0.695macro F1-score) while exceeding the baselineresults by good margins.",
}
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%0 Conference Proceedings
%T Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection
%A Mahadevan, Shankar
%A Benhur, Sean
%A Nayak, Roshan
%A Subramanian, Malliga
%A Shanmugavadivel, Kogilavani
%A Sivanraju, Kanchana
%A Chakravarthi, Bharathi Raja
%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 mahadevan-etal-2022-transformers
%X Social media is an idea created to make theworld smaller and more connected. Recently,it has become a hub of fake news and sexistmemes that target women. Social Media shouldensure proper women’s safety and equality. Filteringsuch information from social media is ofparamount importance to achieving this goal. In this paper, we describe the system developedby our team for SemEval-2022 Task 5: MultimediaAutomatic Misogyny Identification. Wepropose a multimodal training methodologythat achieves good performance on both thesubtasks, ranking 4th for Subtask A (0.718macro F1-score) and 9th for Subtask B (0.695macro F1-score) while exceeding the baselineresults by good margins.
%R 10.18653/v1/2022.semeval-1.75
%U https://aclanthology.org/2022.semeval-1.75
%U https://doi.org/10.18653/v1/2022.semeval-1.75
%P 550-554
Markdown (Informal)
[Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection](https://aclanthology.org/2022.semeval-1.75) (Mahadevan et al., SemEval 2022)
ACL