Summary of Araieval Shared Task: Propagandistic Techniques Detection in Unimodal and Multimodal Arabic Content, by Maram Hasanain et al.
ArAIEval Shared Task: Propagandistic Techniques Detection in Unimodal and Multimodal Arabic Content
by Maram Hasanain, Md. Arid Hasan, Fatema Ahmed, Reem Suwaileh, Md. Rafiul Biswas, Wajdi Zaghouani, Firoj Alam
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The second edition of ArAIEval, a shared task organized at the ArabicNLP 2024 conference, presents two challenges: detecting propagandistic textual spans and persuasion techniques in tweets and news articles, and distinguishing between propagandistic and non-propagandistic memes. Fourteen teams participated in the final evaluation phase, with most using fine-tuned transformer models like AraBERT. The task setup, dataset construction, and evaluation metrics are described, as well as the participating systems. To facilitate further research, all datasets and evaluation scripts are released. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ArAIEval is a shared task that helps researchers study Arabic text analysis. It has two challenges: finding propaganda in tweets and news articles, and identifying memes as true or false. Many teams used special models called AraBERT to help them do this work. The challenge organizers explain how the task works, what data they used, and how they evaluated each team’s results. They also give details about each team’s approach. To make it easier for other researchers to use these challenges, all the data and evaluation tools are available online. |
Keywords
» Artificial intelligence » Transformer
