@inproceedings{das-etal-2020-kafk,
title = "{KAFK} at {S}em{E}val-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify {I}nternet Memes",
author = "Das, Kaushik Amar and
Baruah, Arup and
Barbhuiya, Ferdous Ahmed and
Dey, Kuntal",
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-1.152",
doi = "10.18653/v1/2020.semeval-1.152",
pages = "1148--1154",
abstract = "This paper presents two approaches for the internet meme classification challenge of SemEval-2020 Task 8 by Team KAFK (cosec). The first approach uses both text and image features, while the second approach uses only the images. Error analysis of the two approaches shows that using only the images is more robust to the noise in the text on the memes. We utilize pre-trained DistilBERT and EfficientNet to extract features from the text and image of the memes respectively. Our classification systems obtained macro f1 score of 0.3286 for Task A and 0.5005 for Task B.",
}
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<abstract>This paper presents two approaches for the internet meme classification challenge of SemEval-2020 Task 8 by Team KAFK (cosec). The first approach uses both text and image features, while the second approach uses only the images. Error analysis of the two approaches shows that using only the images is more robust to the noise in the text on the memes. We utilize pre-trained DistilBERT and EfficientNet to extract features from the text and image of the memes respectively. Our classification systems obtained macro f1 score of 0.3286 for Task A and 0.5005 for Task B.</abstract>
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%0 Conference Proceedings
%T KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes
%A Das, Kaushik Amar
%A Baruah, Arup
%A Barbhuiya, Ferdous Ahmed
%A Dey, Kuntal
%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 das-etal-2020-kafk
%X This paper presents two approaches for the internet meme classification challenge of SemEval-2020 Task 8 by Team KAFK (cosec). The first approach uses both text and image features, while the second approach uses only the images. Error analysis of the two approaches shows that using only the images is more robust to the noise in the text on the memes. We utilize pre-trained DistilBERT and EfficientNet to extract features from the text and image of the memes respectively. Our classification systems obtained macro f1 score of 0.3286 for Task A and 0.5005 for Task B.
%R 10.18653/v1/2020.semeval-1.152
%U https://aclanthology.org/2020.semeval-1.152
%U https://doi.org/10.18653/v1/2020.semeval-1.152
%P 1148-1154
Markdown (Informal)
[KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes](https://aclanthology.org/2020.semeval-1.152) (Das et al., SemEval 2020)
ACL