Semantists at ImageArg-2023: Exploring Cross-modal Contrastive and Ensemble Models for Multimodal Stance and Persuasiveness Classification
Kanagasabai Rajaraman, Hariram Veeramani, Saravanan Rajamanickam, Adam Maciej Westerski, Jung-Jae Kim
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.- Anthology ID:
- 2023.argmining-1.20
- Volume:
- Proceedings of the 10th Workshop on Argument Mining
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Milad Alshomary, Chung-Chi Chen, Smaranda Muresan, Joonsuk Park, Julia Romberg
- Venues:
- ArgMining | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 181–186
- Language:
- URL:
- https://aclanthology.org/2023.argmining-1.20
- DOI:
- 10.18653/v1/2023.argmining-1.20
- Cite (ACL):
- Kanagasabai Rajaraman, Hariram Veeramani, Saravanan Rajamanickam, Adam Maciej Westerski, and Jung-Jae Kim. 2023. Semantists at ImageArg-2023: Exploring Cross-modal Contrastive and Ensemble Models for Multimodal Stance and Persuasiveness Classification. In Proceedings of the 10th Workshop on Argument Mining, pages 181–186, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Semantists at ImageArg-2023: Exploring Cross-modal Contrastive and Ensemble Models for Multimodal Stance and Persuasiveness Classification (Rajaraman et al., ArgMining-WS 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.argmining-1.20.pdf