Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining

Zhexiong Liu, Mohamed Elaraby, Yang Zhong, Diane Litman
10/15/2023
cs.CL

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Abstract

This paper presents an overview of the ImageArg shared task, the first multimodal Argument Mining shared task co-located with the 10th Workshop on Argument Mining at EMNLP 2023. The shared task comprises two classification subtasks - (1) Subtask-A: Argument Stance Classification; (2) Subtask-B: Image Persuasiveness Classification. The former determines the stance of a tweet containing an image and a piece of text toward a controversial topic (e.g., gun control and abortion). The latter determines whether the image makes the tweet text more persuasive. The shared task received 31 submissions for Subtask-A and 21 submissions for Subtask-B from 9 different teams across 6 countries. The top submission in Subtask-A achieved an F1-score of 0.8647 while the best submission in Subtask-B achieved an F1-score of 0.5561.

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Cite this paper

@article{liu2023overview,
  title  = {Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining},
  author = {Zhexiong Liu and Mohamed Elaraby and Yang Zhong and Diane Litman},
  year   = {2023},
  eprint = {2310.12172},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.12172v2}
}

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