@inproceedings{muti-etal-2022-unibo,
title = "{U}ni{BO} at {S}em{E}val-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes",
author = "Muti, Arianna and
Korre, Katerina and
Barr{\'o}n-Cede{\~n}o, Alberto",
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-1.91",
doi = "10.18653/v1/2022.semeval-1.91",
pages = "663--672",
abstract = "We present our submission to SemEval 2022 Task 5 on Multimedia Automatic Misogyny Identification. We address the two tasks: Task A consists of identifying whether a meme is misogynous. If so, Task B attempts to identify its kind among shaming, stereotyping, objectification, and violence. Our approach combines a BERT Transformer with CLIP for the textual and visual representations. Both textual and visual encoders are fused in an early-fusion fashion through a Multimodal Bidirectional Transformer with unimodally pretrained components. Our official submissions obtain macro-averaged F$_1$=0.727 in Task A (4th position out of 69 participants)and weighted F$_1$=0.710 in Task B (4th position out of 42 participants).",
}
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%0 Conference Proceedings
%T UniBO at SemEval-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes
%A Muti, Arianna
%A Korre, Katerina
%A Barrón-Cedeño, Alberto
%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 muti-etal-2022-unibo
%X We present our submission to SemEval 2022 Task 5 on Multimedia Automatic Misogyny Identification. We address the two tasks: Task A consists of identifying whether a meme is misogynous. If so, Task B attempts to identify its kind among shaming, stereotyping, objectification, and violence. Our approach combines a BERT Transformer with CLIP for the textual and visual representations. Both textual and visual encoders are fused in an early-fusion fashion through a Multimodal Bidirectional Transformer with unimodally pretrained components. Our official submissions obtain macro-averaged F₁=0.727 in Task A (4th position out of 69 participants)and weighted F₁=0.710 in Task B (4th position out of 42 participants).
%R 10.18653/v1/2022.semeval-1.91
%U https://aclanthology.org/2022.semeval-1.91
%U https://doi.org/10.18653/v1/2022.semeval-1.91
%P 663-672
Markdown (Informal)
[UniBO at SemEval-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes](https://aclanthology.org/2022.semeval-1.91) (Muti et al., SemEval 2022)
ACL