@inproceedings{sharma-etal-2022-iitr,
title = "{IITR} {C}ode{B}usters at {S}em{E}val-2022 Task 5: Misogyny Identification using Transformers",
author = "Sharma, Gagan and
Sunil Gitte, Gajanan and
Goyal, Shlok and
Sharma, Raksha",
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-1.100",
doi = "10.18653/v1/2022.semeval-1.100",
pages = "728--732",
abstract = "This paper presents our submission to task 5 ( Multimedia Automatic Misogyny Identification) of the SemEval 2022 competition. The purpose of the task is to identify given memes as misogynistic or not and further label the type of misogyny involved. In this paper, we present our approach based on language processing tools. We embed meme texts using GloVe embedding and classify misogyny using BERT model. Our model obtains an F1-score of 66.24{\%} and 63.5{\%} in misogyny classification and misogyny labels, respectively.",
}
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<abstract>This paper presents our submission to task 5 ( Multimedia Automatic Misogyny Identification) of the SemEval 2022 competition. The purpose of the task is to identify given memes as misogynistic or not and further label the type of misogyny involved. In this paper, we present our approach based on language processing tools. We embed meme texts using GloVe embedding and classify misogyny using BERT model. Our model obtains an F1-score of 66.24% and 63.5% in misogyny classification and misogyny labels, respectively.</abstract>
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%0 Conference Proceedings
%T IITR CodeBusters at SemEval-2022 Task 5: Misogyny Identification using Transformers
%A Sharma, Gagan
%A Sunil Gitte, Gajanan
%A Goyal, Shlok
%A Sharma, Raksha
%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 sharma-etal-2022-iitr
%X This paper presents our submission to task 5 ( Multimedia Automatic Misogyny Identification) of the SemEval 2022 competition. The purpose of the task is to identify given memes as misogynistic or not and further label the type of misogyny involved. In this paper, we present our approach based on language processing tools. We embed meme texts using GloVe embedding and classify misogyny using BERT model. Our model obtains an F1-score of 66.24% and 63.5% in misogyny classification and misogyny labels, respectively.
%R 10.18653/v1/2022.semeval-1.100
%U https://aclanthology.org/2022.semeval-1.100
%U https://doi.org/10.18653/v1/2022.semeval-1.100
%P 728-732
Markdown (Informal)
[IITR CodeBusters at SemEval-2022 Task 5: Misogyny Identification using Transformers](https://aclanthology.org/2022.semeval-1.100) (Sharma et al., SemEval 2022)
ACL