@inproceedings{srivastava-2022-poirot-semeval,
title = "Poirot at {S}em{E}val-2022 Task 5: Leveraging Graph Network for Misogynistic Meme Detection",
author = "Srivastava, Harshvardhan",
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-1.110/",
doi = "10.18653/v1/2022.semeval-1.110",
pages = "793--801",
abstract = "In recent years, there has been an upsurge in a new form of entertainment medium called memes. These memes although seemingly innocuous have transcended the boundary of online harassment against women and created an unwanted bias against them. To help alleviate this problem, we propose an early fusion model for the prediction and identification of misogynistic memes and their type in this paper for which we participated in SemEval-2022 Task 5. The model receives as input meme image with its text transcription with a target vector. Given that a key challenge with this task is the combination of different modalities to predict misogyny, our model relies on pre-trained contextual representations from different state-of-the-art transformer-based language models and pre-trained image models to get an effective image representation. Our model achieved competitive results on both SubTask-A and SubTask-B with the other competingteams and significantly outperforms the baselines."
}
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<abstract>In recent years, there has been an upsurge in a new form of entertainment medium called memes. These memes although seemingly innocuous have transcended the boundary of online harassment against women and created an unwanted bias against them. To help alleviate this problem, we propose an early fusion model for the prediction and identification of misogynistic memes and their type in this paper for which we participated in SemEval-2022 Task 5. The model receives as input meme image with its text transcription with a target vector. Given that a key challenge with this task is the combination of different modalities to predict misogyny, our model relies on pre-trained contextual representations from different state-of-the-art transformer-based language models and pre-trained image models to get an effective image representation. Our model achieved competitive results on both SubTask-A and SubTask-B with the other competingteams and significantly outperforms the baselines.</abstract>
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%0 Conference Proceedings
%T Poirot at SemEval-2022 Task 5: Leveraging Graph Network for Misogynistic Meme Detection
%A Srivastava, Harshvardhan
%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 srivastava-2022-poirot-semeval
%X In recent years, there has been an upsurge in a new form of entertainment medium called memes. These memes although seemingly innocuous have transcended the boundary of online harassment against women and created an unwanted bias against them. To help alleviate this problem, we propose an early fusion model for the prediction and identification of misogynistic memes and their type in this paper for which we participated in SemEval-2022 Task 5. The model receives as input meme image with its text transcription with a target vector. Given that a key challenge with this task is the combination of different modalities to predict misogyny, our model relies on pre-trained contextual representations from different state-of-the-art transformer-based language models and pre-trained image models to get an effective image representation. Our model achieved competitive results on both SubTask-A and SubTask-B with the other competingteams and significantly outperforms the baselines.
%R 10.18653/v1/2022.semeval-1.110
%U https://aclanthology.org/2022.semeval-1.110/
%U https://doi.org/10.18653/v1/2022.semeval-1.110
%P 793-801
Markdown (Informal)
[Poirot at SemEval-2022 Task 5: Leveraging Graph Network for Misogynistic Meme Detection](https://aclanthology.org/2022.semeval-1.110/) (Srivastava, SemEval 2022)
ACL