KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes

Kaushik Amar Das, Arup Baruah, Ferdous Ahmed Barbhuiya, Kuntal Dey


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.
Anthology ID:
2020.semeval-1.152
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
*SEMEVAL | COLING
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1148–1154
Language:
URL:
https://www.aclweb.org/anthology/2020.semeval-1.152
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.semeval-1.152.pdf