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.

本研究论文概述了 ImageArg 共享任务，这是第一个与 EMNLP 2023 第 10 届 Argument Mining 研讨会共同举办的多模态论证挖掘共享任务。共享任务包括两个分类子任务 -（1）子任务 A：论证立场分类；（2）子任务 B：图像说服力分类。前者确定包含图像和文本的推文对有争议的主题（例如枪支管制和堕胎）的立场。后者确定图像是否使推文文本更具说服力。共享任务共收到来自 6 个国家的 9 个不同团队的 31 个子任务 A 提交和 21 个子任务 B 提交。子任务 A 中最高分提交的 F1 得分为 0.8647，而子任务 B 中最佳提交的 F1 得分为 0.5561。

Multimodal Argument Mining 中首个共享任务：ImageArg-2023 综述

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

The growing interest in developing corpora of persuasive texts has promoted
applications in automated systems, e.g., debating and essay scoring systems;
however, there is little prior work mining image persuasiveness from an
argumentative perspective. To expand persuasiveness mining into a multi-modal
realm, we present a multi-modal dataset, ImageArg, consisting of annotations of
image persuasiveness in tweets. The annotations are based on a persuasion
taxonomy we developed to explore image functionalities and the means of
persuasion. We benchmark image persuasiveness tasks on ImageArg using
widely-used multi-modal learning methods. The experimental results show that
our dataset offers a useful resource for this rich and challenging topic, and
there is ample room for modeling improvement.

本研究的重点在于将具有说服力的图像转换为多模式数据，开发用于自动化系统的多模式数据集，然后使用这个数据集对图像说服力任务进行基准测试和实验，通过开发一种说服分类方法来划分图像的说服功能和说服方式，为这一高级领域提供有用的资源。