Abstract
This paper presents the solution proposed by the 1213Li team for subtask 3 in SemEval-2021 Task 6: identifying the multiple persuasion techniques used in the multi-modal content of the meme. We explored various approaches in feature extraction and the detection of persuasion labels. Our final model employs pre-trained models including RoBERTa and ResNet-50 as a feature extractor for texts and images, respectively, and adopts a label embedding layer with multi-modal attention mechanism to measure the similarity of labels with the multi-modal information and fuse features for label prediction. Our proposed method outperforms the provided baseline method and achieves 3rd out of 16 participants with 0.54860/0.22830 for Micro/Macro F1 scores.- Anthology ID:
- 2021.semeval-1.142
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1032–1036
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.142
- DOI:
- 10.18653/v1/2021.semeval-1.142
- Cite (ACL):
- Peiguang Li, Xuan Li, and Xian Sun. 2021. 1213Li at SemEval-2021 Task 6: Detection of Propaganda with Multi-modal Attention and Pre-trained Models. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1032–1036, Online. Association for Computational Linguistics.
- Cite (Informal):
- 1213Li at SemEval-2021 Task 6: Detection of Propaganda with Multi-modal Attention and Pre-trained Models (Li et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2021.semeval-1.142.pdf