@inproceedings{rajaraman-etal-2023-semantists,
title = "Semantists at {I}mage{A}rg-2023: Exploring Cross-modal Contrastive and Ensemble Models for Multimodal Stance and Persuasiveness Classification",
author = "Rajaraman, Kanagasabai and
Veeramani, Hariram and
Rajamanickam, Saravanan and
Westerski, Adam Maciej and
Kim, Jung-Jae",
editor = "Alshomary, Milad and
Chen, Chung-Chi and
Muresan, Smaranda and
Park, Joonsuk and
Romberg, Julia",
booktitle = "Proceedings of the 10th Workshop on Argument Mining",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.argmining-1.20/",
doi = "10.18653/v1/2023.argmining-1.20",
pages = "181--186",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Semantists at ImageArg-2023: Exploring Cross-modal Contrastive and Ensemble Models for Multimodal Stance and Persuasiveness Classification
%A Rajaraman, Kanagasabai
%A Veeramani, Hariram
%A Rajamanickam, Saravanan
%A Westerski, Adam Maciej
%A Kim, Jung-Jae
%Y Alshomary, Milad
%Y Chen, Chung-Chi
%Y Muresan, Smaranda
%Y Park, Joonsuk
%Y Romberg, Julia
%S Proceedings of the 10th Workshop on Argument Mining
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F rajaraman-etal-2023-semantists
%X 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.
%R 10.18653/v1/2023.argmining-1.20
%U https://aclanthology.org/2023.argmining-1.20/
%U https://doi.org/10.18653/v1/2023.argmining-1.20
%P 181-186
Markdown (Informal)
[Semantists at ImageArg-2023: Exploring Cross-modal Contrastive and Ensemble Models for Multimodal Stance and Persuasiveness Classification](https://aclanthology.org/2023.argmining-1.20/) (Rajaraman et al., ArgMining 2023)
ACL