@inproceedings{gu-etal-2022-qinian,
title = "{Q}i{N}i{A}n at {S}em{E}val-2022 Task 5: Multi-Modal Misogyny Detection and Classification",
author = "Gu, Qin and
Meisinger, Nino and
Dick, Anna-Katharina",
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-1.102",
doi = "10.18653/v1/2022.semeval-1.102",
pages = "736--741",
abstract = "In this paper, we describe our submission to the misogyny classification challenge at SemEval-2022. We propose two models for the two subtasks of the challenge: The first uses joint image and text classification to classify memes as either misogynistic or not. This model uses a majority voting ensemble structure built on traditional classifiers and additional image information such as age, gender and nudity estimations. The second model uses a RoBERTa classifier on the text transcriptions to additionally identify the type of problematic ideas the memes perpetuate. Our submissions perform above all organizer submitted baselines. For binary misogyny classification, our system achieved the fifth place on the leaderboard, with a macro F1-score of 0.665. For multi-label classification identifying the type of misogyny, our model achieved place 19 on the leaderboard, with a weighted F1-score of 0.637.",
}
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%0 Conference Proceedings
%T QiNiAn at SemEval-2022 Task 5: Multi-Modal Misogyny Detection and Classification
%A Gu, Qin
%A Meisinger, Nino
%A Dick, Anna-Katharina
%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 gu-etal-2022-qinian
%X In this paper, we describe our submission to the misogyny classification challenge at SemEval-2022. We propose two models for the two subtasks of the challenge: The first uses joint image and text classification to classify memes as either misogynistic or not. This model uses a majority voting ensemble structure built on traditional classifiers and additional image information such as age, gender and nudity estimations. The second model uses a RoBERTa classifier on the text transcriptions to additionally identify the type of problematic ideas the memes perpetuate. Our submissions perform above all organizer submitted baselines. For binary misogyny classification, our system achieved the fifth place on the leaderboard, with a macro F1-score of 0.665. For multi-label classification identifying the type of misogyny, our model achieved place 19 on the leaderboard, with a weighted F1-score of 0.637.
%R 10.18653/v1/2022.semeval-1.102
%U https://aclanthology.org/2022.semeval-1.102
%U https://doi.org/10.18653/v1/2022.semeval-1.102
%P 736-741
Markdown (Informal)
[QiNiAn at SemEval-2022 Task 5: Multi-Modal Misogyny Detection and Classification](https://aclanthology.org/2022.semeval-1.102) (Gu et al., SemEval 2022)
ACL