@inproceedings{yu-etal-2022-rubcsg,
title = "{R}ub{CSG} at {S}em{E}val-2022 Task 5: Ensemble learning for identifying misogynous {MEME}s",
author = {Yu, Wentao and
Boenninghoff, Benedikt and
R{\"o}hrig, Jonas and
Kolossa, Dorothea},
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-1.86",
doi = "10.18653/v1/2022.semeval-1.86",
pages = "626--635",
abstract = "This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification. The challenge organizers provide an English meme dataset to develop and train systems for identifying and classifying misogynous memes. More precisely, the competition is separated into two sub-tasks: sub-task A asks for a binary decision as to whether a meme expresses misogyny, while sub-task B is to classify misogynous memes into the potentially overlapping sub-categories of stereotype, shaming, objectification, and violence. For our submission, we implement a new model fusion network and employ an ensemble learning approach for better performance. With this structure, we achieve a 0.755 macro-average F1-score (11th) in sub-task A and a 0.709 weighted-average F1-score (10th) in sub-task B.",
}
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<abstract>This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification. The challenge organizers provide an English meme dataset to develop and train systems for identifying and classifying misogynous memes. More precisely, the competition is separated into two sub-tasks: sub-task A asks for a binary decision as to whether a meme expresses misogyny, while sub-task B is to classify misogynous memes into the potentially overlapping sub-categories of stereotype, shaming, objectification, and violence. For our submission, we implement a new model fusion network and employ an ensemble learning approach for better performance. With this structure, we achieve a 0.755 macro-average F1-score (11th) in sub-task A and a 0.709 weighted-average F1-score (10th) in sub-task B.</abstract>
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%0 Conference Proceedings
%T RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs
%A Yu, Wentao
%A Boenninghoff, Benedikt
%A Röhrig, Jonas
%A Kolossa, Dorothea
%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 yu-etal-2022-rubcsg
%X This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification. The challenge organizers provide an English meme dataset to develop and train systems for identifying and classifying misogynous memes. More precisely, the competition is separated into two sub-tasks: sub-task A asks for a binary decision as to whether a meme expresses misogyny, while sub-task B is to classify misogynous memes into the potentially overlapping sub-categories of stereotype, shaming, objectification, and violence. For our submission, we implement a new model fusion network and employ an ensemble learning approach for better performance. With this structure, we achieve a 0.755 macro-average F1-score (11th) in sub-task A and a 0.709 weighted-average F1-score (10th) in sub-task B.
%R 10.18653/v1/2022.semeval-1.86
%U https://aclanthology.org/2022.semeval-1.86
%U https://doi.org/10.18653/v1/2022.semeval-1.86
%P 626-635
Markdown (Informal)
[RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs](https://aclanthology.org/2022.semeval-1.86) (Yu et al., SemEval 2022)
ACL