TechSSN at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models

Rajalakshmi Sivanaiah, Angel S, Sakaya Milton Rajendram, Mirnalinee T T


Abstract
Research is progressing in a fast manner in the field of offensive, hate speech, abusive and sarcastic data. Tackling hate speech against women is urgent and really needed to give respect to the lady of our life. This paper describes the system used for identifying misogynous content using images and text. The system developed by the team TECHSSN uses transformer models to detect the misogynous content from text and Convolutional Neural Network model for image data. Various models like BERT, ALBERT, XLNET and CNN are explored and the combination of ALBERT and CNN as an ensemble model provides better results than the rest. This system was developed for the task 5 of the competition, SemEval 2022.
Anthology ID:
2022.semeval-1.78
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
571–574
Language:
URL:
https://aclanthology.org/2022.semeval-1.78
DOI:
10.18653/v1/2022.semeval-1.78
Bibkey:
Cite (ACL):
Rajalakshmi Sivanaiah, Angel S, Sakaya Milton Rajendram, and Mirnalinee T T. 2022. TechSSN at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 571–574, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
TechSSN at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models (Sivanaiah et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.78.pdf