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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.semeval-1.155.pdf