@inproceedings{tao-kim-2022-taochen,
title = "taochen at {S}em{E}val-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning",
author = "Tao, Chen and
Kim, Jung-jae",
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-1.89",
doi = "10.18653/v1/2022.semeval-1.89",
pages = "648--653",
abstract = "We present a multi-modal deep learning system for the Multimedia Automatic Misogyny Identification (MAMI) challenge, a SemEval task of identifying and classifying misogynistic messages in online memes. We adapt multi-task learning for the multimodal subtasks of the MAMI challenge to transfer knowledge among the correlated subtasks. We also leverage on ensemble learning for synergistic integration of models individually trained for the subtasks. We finally discuss about errors of the system to provide useful insights for future work.",
}
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<abstract>We present a multi-modal deep learning system for the Multimedia Automatic Misogyny Identification (MAMI) challenge, a SemEval task of identifying and classifying misogynistic messages in online memes. We adapt multi-task learning for the multimodal subtasks of the MAMI challenge to transfer knowledge among the correlated subtasks. We also leverage on ensemble learning for synergistic integration of models individually trained for the subtasks. We finally discuss about errors of the system to provide useful insights for future work.</abstract>
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%0 Conference Proceedings
%T taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning
%A Tao, Chen
%A Kim, Jung-jae
%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 tao-kim-2022-taochen
%X We present a multi-modal deep learning system for the Multimedia Automatic Misogyny Identification (MAMI) challenge, a SemEval task of identifying and classifying misogynistic messages in online memes. We adapt multi-task learning for the multimodal subtasks of the MAMI challenge to transfer knowledge among the correlated subtasks. We also leverage on ensemble learning for synergistic integration of models individually trained for the subtasks. We finally discuss about errors of the system to provide useful insights for future work.
%R 10.18653/v1/2022.semeval-1.89
%U https://aclanthology.org/2022.semeval-1.89
%U https://doi.org/10.18653/v1/2022.semeval-1.89
%P 648-653
Markdown (Informal)
[taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning](https://aclanthology.org/2022.semeval-1.89) (Tao & Kim, SemEval 2022)
ACL