@inproceedings{fersini-etal-2022-semeval,
title = "{S}em{E}val-2022 Task 5: Multimedia Automatic Misogyny Identification",
author = "Fersini, Elisabetta and
Gasparini, Francesca and
Rizzi, Giulia and
Saibene, Aurora and
Chulvi, Berta and
Rosso, Paolo and
Lees, Alyssa and
Sorensen, Jeffrey",
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-1.74/",
doi = "10.18653/v1/2022.semeval-1.74",
pages = "533--549",
abstract = "The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI),which explores the detection of misogynous memes on the web by taking advantage of available texts and images. The task has been organised in two related sub-tasks: the first one is focused on recognising whether a meme is misogynous or not (Sub-task A), while the second one is devoted to recognising types of misogyny (Sub-task B). MAMI has been one of the most popular tasks at SemEval-2022 with more than 400 participants, 65 teams involved in Sub-task A and 41 in Sub-task B from 13 countries. The MAMI challenge received 4214 submitted runs (of which 166 uploaded on the leader-board), denoting an enthusiastic participation for the proposed problem. The collection and annotation is described for the task dataset. The paper provides an overview of the systems proposed for the challenge, reports the results achieved in both sub-tasks and outlines a description of the main errors for a comprehension of the systems capabilities and for detailing future research perspectives."
}
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<abstract>The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI),which explores the detection of misogynous memes on the web by taking advantage of available texts and images. The task has been organised in two related sub-tasks: the first one is focused on recognising whether a meme is misogynous or not (Sub-task A), while the second one is devoted to recognising types of misogyny (Sub-task B). MAMI has been one of the most popular tasks at SemEval-2022 with more than 400 participants, 65 teams involved in Sub-task A and 41 in Sub-task B from 13 countries. The MAMI challenge received 4214 submitted runs (of which 166 uploaded on the leader-board), denoting an enthusiastic participation for the proposed problem. The collection and annotation is described for the task dataset. The paper provides an overview of the systems proposed for the challenge, reports the results achieved in both sub-tasks and outlines a description of the main errors for a comprehension of the systems capabilities and for detailing future research perspectives.</abstract>
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%0 Conference Proceedings
%T SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
%A Fersini, Elisabetta
%A Gasparini, Francesca
%A Rizzi, Giulia
%A Saibene, Aurora
%A Chulvi, Berta
%A Rosso, Paolo
%A Lees, Alyssa
%A Sorensen, Jeffrey
%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 fersini-etal-2022-semeval
%X The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI),which explores the detection of misogynous memes on the web by taking advantage of available texts and images. The task has been organised in two related sub-tasks: the first one is focused on recognising whether a meme is misogynous or not (Sub-task A), while the second one is devoted to recognising types of misogyny (Sub-task B). MAMI has been one of the most popular tasks at SemEval-2022 with more than 400 participants, 65 teams involved in Sub-task A and 41 in Sub-task B from 13 countries. The MAMI challenge received 4214 submitted runs (of which 166 uploaded on the leader-board), denoting an enthusiastic participation for the proposed problem. The collection and annotation is described for the task dataset. The paper provides an overview of the systems proposed for the challenge, reports the results achieved in both sub-tasks and outlines a description of the main errors for a comprehension of the systems capabilities and for detailing future research perspectives.
%R 10.18653/v1/2022.semeval-1.74
%U https://aclanthology.org/2022.semeval-1.74/
%U https://doi.org/10.18653/v1/2022.semeval-1.74
%P 533-549
Markdown (Informal)
[SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification](https://aclanthology.org/2022.semeval-1.74/) (Fersini et al., SemEval 2022)
ACL
- Elisabetta Fersini, Francesca Gasparini, Giulia Rizzi, Aurora Saibene, Berta Chulvi, Paolo Rosso, Alyssa Lees, and Jeffrey Sorensen. 2022. SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 533–549, Seattle, United States. Association for Computational Linguistics.