RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs
Wentao Yu, Benedikt Boenninghoff, Jonas Röhrig, Dorothea Kolossa
Abstract
This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification. The challenge organizers provide an English meme dataset to develop and train systems for identifying and classifying misogynous memes. More precisely, the competition is separated into two sub-tasks: sub-task A asks for a binary decision as to whether a meme expresses misogyny, while sub-task B is to classify misogynous memes into the potentially overlapping sub-categories of stereotype, shaming, objectification, and violence. For our submission, we implement a new model fusion network and employ an ensemble learning approach for better performance. With this structure, we achieve a 0.755 macro-average F1-score (11th) in sub-task A and a 0.709 weighted-average F1-score (10th) in sub-task B.- Anthology ID:
- 2022.semeval-1.86
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 626–635
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.86
- DOI:
- 10.18653/v1/2022.semeval-1.86
- Cite (ACL):
- Wentao Yu, Benedikt Boenninghoff, Jonas Röhrig, and Dorothea Kolossa. 2022. RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 626–635, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs (Yu et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.semeval-1.86.pdf
- Code
- rub-ksv/semeval-task5-mami
- Data
- Hateful Memes