Abstract
We present our submission to SemEval 2022 Task 5 on Multimedia Automatic Misogyny Identification. We address the two tasks: Task A consists of identifying whether a meme is misogynous. If so, Task B attempts to identify its kind among shaming, stereotyping, objectification, and violence. Our approach combines a BERT Transformer with CLIP for the textual and visual representations. Both textual and visual encoders are fused in an early-fusion fashion through a Multimodal Bidirectional Transformer with unimodally pretrained components. Our official submissions obtain macro-averaged F1=0.727 in Task A (4th position out of 69 participants)and weighted F1=0.710 in Task B (4th position out of 42 participants).- Anthology ID:
- 2022.semeval-1.91
- 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:
- 663–672
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.91
- DOI:
- 10.18653/v1/2022.semeval-1.91
- Cite (ACL):
- Arianna Muti, Katerina Korre, and Alberto Barrón-Cedeño. 2022. UniBO at SemEval-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 663–672, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- UniBO at SemEval-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes (Muti et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.semeval-1.91.pdf
- Code
- tinffoil/unibo-at-semeval-2022-mami