mlsanityData to 7 Oct 2026

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

arXiv 2310.12172 ↗First posted 15 Oct 2023cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 57 references that are in the map. First posted on arXiv 15 Oct 2023.

Builds on

25 of 57 references.

References · 25

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13 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Can Language Models Recognize Convincing Arguments?cited by 21
  2. 2025Large Language Models in Argument Mining: A Surveycited by 14
  3. 2025Towards a Perspectivist Turn in Argument Quality Assessmentcited by 5
  4. 2026Can MLLMs Reason About Visual Persuasion? Evaluating the Efficacy and Faithfulness of Reasoningcited by 0