IITR CodeBusters at SemEval-2022 Task 5: Misogyny Identification using Transformers

Gagan Sharma, Gajanan Sunil Gitte, Shlok Goyal, Raksha Sharma


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.
Anthology ID:
2022.semeval-1.100
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venues:
NAACL | SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
728–732
Language:
URL:
https://aclanthology.org/2022.semeval-1.100
DOI:
10.18653/v1/2022.semeval-1.100
Bibkey:
Cite (ACL):
Gagan Sharma, Gajanan Sunil Gitte, Shlok Goyal, and Raksha Sharma. 2022. IITR CodeBusters at SemEval-2022 Task 5: Misogyny Identification using Transformers. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 728–732, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
IITR CodeBusters at SemEval-2022 Task 5: Misogyny Identification using Transformers (Sharma et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.100.pdf