Abstract
The growing interest in developing corpora of persuasive texts has promoted applications in automated systems, e.g., debating and essay scoring systems; however, there is little prior work mining image persuasiveness from an argumentative perspective. To expand persuasiveness mining into a multi-modal realm, we present a multi-modal dataset, ImageArg, consisting of annotations of image persuasiveness in tweets. The annotations are based on a persuasion taxonomy we developed to explore image functionalities and the means of persuasion. We benchmark image persuasiveness tasks on ImageArg using widely-used multi-modal learning methods. The experimental results show that our dataset offers a useful resource for this rich and challenging topic, and there is ample room for modeling improvement.- Anthology ID:
- 2022.argmining-1.1
- Volume:
- Proceedings of the 9th Workshop on Argument Mining
- Month:
- October
- Year:
- 2022
- Address:
- Online and in Gyeongju, Republic of Korea
- Venue:
- ArgMining
- SIG:
- Publisher:
- International Conference on Computational Linguistics
- Note:
- Pages:
- 1–18
- Language:
- URL:
- https://aclanthology.org/2022.argmining-1.1
- DOI:
- Cite (ACL):
- Zhexiong Liu, Meiqi Guo, Yue Dai, and Diane Litman. 2022. ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining. In Proceedings of the 9th Workshop on Argument Mining, pages 1–18, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
- Cite (Informal):
- ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining (Liu et al., ArgMining 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.argmining-1.1.pdf
- Code
- meiqiguo/argmining2022-imagearg