@inproceedings{arango-etal-2022-hateu,
title = "{H}ate{U} at {S}em{E}val-2022 Task 5: Multimedia Automatic Misogyny Identification",
author = "Arango, Ayme and
Perez-Martin, Jesus and
Labrada, Arniel",
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-1.80",
doi = "10.18653/v1/2022.semeval-1.80",
pages = "581--584",
abstract = "Hate speech expressions in social media are not limited to textual messages; they can appear in videos, images, or multimodal formats like memes. Existing work towards detecting such expressions has been conducted almost exclusively over textual content, and the analysis of pictures and videos has been very scarce. This paper describes our team proposal in the Multimedia Automatic Misogyny Identification (MAMI) task at SemEval 2022. The challenge consisted of identifying misogynous memes from a dataset where images and text transcriptions were provided. We reported a 71{\%} of F-score using a multimodal system based on the CLIP model.",
}
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%0 Conference Proceedings
%T HateU at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
%A Arango, Ayme
%A Perez-Martin, Jesus
%A Labrada, Arniel
%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 arango-etal-2022-hateu
%X Hate speech expressions in social media are not limited to textual messages; they can appear in videos, images, or multimodal formats like memes. Existing work towards detecting such expressions has been conducted almost exclusively over textual content, and the analysis of pictures and videos has been very scarce. This paper describes our team proposal in the Multimedia Automatic Misogyny Identification (MAMI) task at SemEval 2022. The challenge consisted of identifying misogynous memes from a dataset where images and text transcriptions were provided. We reported a 71% of F-score using a multimodal system based on the CLIP model.
%R 10.18653/v1/2022.semeval-1.80
%U https://aclanthology.org/2022.semeval-1.80
%U https://doi.org/10.18653/v1/2022.semeval-1.80
%P 581-584
Markdown (Informal)
[HateU at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification](https://aclanthology.org/2022.semeval-1.80) (Arango et al., SemEval 2022)
ACL