@inproceedings{liu-etal-2023-overview,
    title = "Overview of {I}mage{A}rg-2023: The First Shared Task in Multimodal Argument Mining",
    author = "Liu, Zhexiong  and
      Elaraby, Mohamed  and
      Zhong, Yang  and
      Litman, Diane",
    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://preview.aclanthology.org/fix-sig-urls/2023.argmining-1.12/",
    doi = "10.18653/v1/2023.argmining-1.12",
    pages = "120--132",
    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."
}