Abstract
In this paper, we describe our submission to the misogyny classification challenge at SemEval-2022. We propose two models for the two subtasks of the challenge: The first uses joint image and text classification to classify memes as either misogynistic or not. This model uses a majority voting ensemble structure built on traditional classifiers and additional image information such as age, gender and nudity estimations. The second model uses a RoBERTa classifier on the text transcriptions to additionally identify the type of problematic ideas the memes perpetuate. Our submissions perform above all organizer submitted baselines. For binary misogyny classification, our system achieved the fifth place on the leaderboard, with a macro F1-score of 0.665. For multi-label classification identifying the type of misogyny, our model achieved place 19 on the leaderboard, with a weighted F1-score of 0.637.- Anthology ID:
- 2022.semeval-1.102
- 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:
- 736–741
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.102
- DOI:
- 10.18653/v1/2022.semeval-1.102
- Cite (ACL):
- Qin Gu, Nino Meisinger, and Anna-Katharina Dick. 2022. QiNiAn at SemEval-2022 Task 5: Multi-Modal Misogyny Detection and Classification. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 736–741, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- QiNiAn at SemEval-2022 Task 5: Multi-Modal Misogyny Detection and Classification (Gu et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.semeval-1.102.pdf