MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes

Irina Bejan


Abstract
Internet memes are one of the most viral types of content in social media and are equally used in promoting hate speech. Towards a more broad understanding of memes, this paper describes the MemoSys system submitted in Task 8 of SemEval 2020, which aims to classify the sentiment of Internet memes and provide a minimum description of the type of humor it depicts (sarcastic, humorous, offensive, motivational) and its semantic scale. The solution presented covers four deep model architectures which are based on a joint fusion between the VGG16 pre-trained model for extracting visual information and the canonical BERT model or TF-IDF for text understanding. The system placed 5th of 36 participating systems in the task A, offering promising prospects to the use of transfer learning to approach Internet memes understanding.
Anthology ID:
2020.semeval-1.155
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:
1172–1178
Language:
URL:
https://aclanthology.org/2020.semeval-1.155
DOI:
10.18653/v1/2020.semeval-1.155
Bibkey:
Cite (ACL):
Irina Bejan. 2020. MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1172–1178, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes (Bejan, SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.155.pdf