@inproceedings{li-etal-2020-cn,
title = "{CN}-{HIT}-{MI}.{T} at {S}em{E}val-2020 Task 8: Memotion Analysis Based on {BERT}",
author = "Li, Zhen and
Zhang, Yaojie and
Xu, Bing and
Zhao, Tiejun",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.145/",
doi = "10.18653/v1/2020.semeval-1.145",
pages = "1100--1105",
abstract = "Internet memes emotion recognition is focused by many researchers. In this paper, we adopt BERT and ResNet for evaluation of detecting the emotions of Internet memes. We focus on solving the problem of data imbalance and data contains noise. We use RandAugment to enhance the data of the picture, and use Training Signal Annealing (TSA) to solve the impact of the imbalance of the label. At the same time, a new loss function is designed to ensure that the model is not affected by input noise which will improve the robustness of the model. We participated in sub-task a and our model based on BERT obtains 34.58{\%} macro F1 score, ranking 10/32."
}
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<abstract>Internet memes emotion recognition is focused by many researchers. In this paper, we adopt BERT and ResNet for evaluation of detecting the emotions of Internet memes. We focus on solving the problem of data imbalance and data contains noise. We use RandAugment to enhance the data of the picture, and use Training Signal Annealing (TSA) to solve the impact of the imbalance of the label. At the same time, a new loss function is designed to ensure that the model is not affected by input noise which will improve the robustness of the model. We participated in sub-task a and our model based on BERT obtains 34.58% macro F1 score, ranking 10/32.</abstract>
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%0 Conference Proceedings
%T CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT
%A Li, Zhen
%A Zhang, Yaojie
%A Xu, Bing
%A Zhao, Tiejun
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F li-etal-2020-cn
%X Internet memes emotion recognition is focused by many researchers. In this paper, we adopt BERT and ResNet for evaluation of detecting the emotions of Internet memes. We focus on solving the problem of data imbalance and data contains noise. We use RandAugment to enhance the data of the picture, and use Training Signal Annealing (TSA) to solve the impact of the imbalance of the label. At the same time, a new loss function is designed to ensure that the model is not affected by input noise which will improve the robustness of the model. We participated in sub-task a and our model based on BERT obtains 34.58% macro F1 score, ranking 10/32.
%R 10.18653/v1/2020.semeval-1.145
%U https://aclanthology.org/2020.semeval-1.145/
%U https://doi.org/10.18653/v1/2020.semeval-1.145
%P 1100-1105
Markdown (Informal)
[CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT](https://aclanthology.org/2020.semeval-1.145/) (Li et al., SemEval 2020)
ACL