Rajaraman, K., Veeramani, H., Rajamanickam, S., Westerski, A. M., & Kim, J.-J. (2023). Semantists at ImageArg-2023: Exploring Cross-modal Contrastive and Ensemble Models for Multimodal Stance and Persuasiveness Classification. Proceedings of the 10th Workshop on Argument Mining. https://doi.org/10.18653/v1/2023.argmining-1.20
Abstract:
In this paper, we describe our system for ImageArg-2023 Shared Task that aims to identify an image's stance towards a tweet and determine its persuasiveness score concerning a specific topic. In particular, the Shared Task proposes two subtasks viz. subtask (A) Multimodal Argument Stance
(AS) Classification, and subtask (B) Multimodal Image Persuasiveness (IP) Classification, using a dataset composed of tweets (images and text) from controversial topics, namely gun control and abortion. For subtask A, we employ multiple transformer models using a text based approach to classify the argumentative stance of the tweet. For sub task B we adopted text based as well as multimodal learning methods to classify image persuasiveness of the tweet. Surprisingly, the text-based approach of the tweet overall performed better than the multimodal approaches considered. In summary, our best system achieved a F1 score of 0.85 for sub task (A) and 0.50 for subtask (B), and ranked 2nd in subtask (A) and 4th in subtask (B), among all teams submissions.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
There was no specific funding for the research done