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    <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.</abstract>
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%0 Conference Proceedings
%T 1213Li at SemEval-2021 Task 6: Detection of Propaganda with Multi-modal Attention and Pre-trained Models
%A Li, Peiguang
%A Li, Xuan
%A Sun, Xian
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F li-etal-2021-1213li
%X 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.
%R 10.18653/v1/2021.semeval-1.142
%U https://aclanthology.org/2021.semeval-1.142
%U https://doi.org/10.18653/v1/2021.semeval-1.142
%P 1032-1036

Markdown (Informal)

[1213Li at SemEval-2021 Task 6: Detection of Propaganda with Multi-modal Attention and Pre-trained Models](https://aclanthology.org/2021.semeval-1.142) (Li et al., SemEval 2021)

• 1213Li at SemEval-2021 Task 6: Detection of Propaganda with Multi-modal Attention and Pre-trained Models (Li et al., SemEval 2021)

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.