@inproceedings{sivanaiah-etal-2022-techssn,
title = "{T}ech{SSN} at {S}em{E}val-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models",
author = "Sivanaiah, Rajalakshmi and
S, Angel and
Rajendram, Sakaya Milton and
T T, Mirnalinee",
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-1.78",
doi = "10.18653/v1/2022.semeval-1.78",
pages = "571--574",
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.",
}
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%0 Conference Proceedings
%T TechSSN at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models
%A Sivanaiah, Rajalakshmi
%A S, Angel
%A Rajendram, Sakaya Milton
%A T T, Mirnalinee
%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 sivanaiah-etal-2022-techssn
%X 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.
%R 10.18653/v1/2022.semeval-1.78
%U https://aclanthology.org/2022.semeval-1.78
%U https://doi.org/10.18653/v1/2022.semeval-1.78
%P 571-574
Markdown (Informal)
[TechSSN at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models](https://aclanthology.org/2022.semeval-1.78) (Sivanaiah et al., SemEval 2022)
ACL