Abstract
We present a multi-modal deep learning system for the Multimedia Automatic Misogyny Identification (MAMI) challenge, a SemEval task of identifying and classifying misogynistic messages in online memes. We adapt multi-task learning for the multimodal subtasks of the MAMI challenge to transfer knowledge among the correlated subtasks. We also leverage on ensemble learning for synergistic integration of models individually trained for the subtasks. We finally discuss about errors of the system to provide useful insights for future work.- Anthology ID:
- 2022.semeval-1.89
- 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:
- 648–653
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.89
- DOI:
- 10.18653/v1/2022.semeval-1.89
- Cite (ACL):
- Chen Tao and Jung-jae Kim. 2022. taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 648–653, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning (Tao & Kim, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.semeval-1.89.pdf