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)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1148–1154
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.152
- DOI:
- 10.18653/v1/2020.semeval-1.152
- Cite (ACL):
- Kaushik Amar Das, Arup Baruah, Ferdous Ahmed Barbhuiya, and Kuntal Dey. 2020. KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1148–1154, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes (Das et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.semeval-1.152.pdf
- Code
- cozek/memotion2020-code