# Legend at ArAIEval Shared Task: Persuasion Technique Detection using a Language-Agnostic Text Representation Model

arXiv: https://arxiv.org/abs/2310.09661 · HTML: https://mlsanity.com/paper/2310.09661/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic](https://mlsanity.com/paper/2211.10057/) (2022)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Volta at SemEval-2021 Task 6: Towards Detecting Persuasive Texts and Images using Textual and Multimodal Ensemble](https://mlsanity.com/paper/2106.00240/) (2021)
- [QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Models](https://mlsanity.com/paper/2305.03336/) (2023)
- [KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection](https://mlsanity.com/paper/2304.11924/) (2023)
- [Transformer-based approaches to Sentiment Detection](https://mlsanity.com/paper/2303.07292/) (2023)

## Built on by (most cited first, 4 of 4)

- [ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text](https://mlsanity.com/paper/2311.03179/) (2023)
- [MedAI Dialog Corpus (MEDIC): Zero-Shot Classification of Doctor and AI Responses in Health Consultations](https://mlsanity.com/paper/2310.12489/) (2023)
- [Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models](https://mlsanity.com/paper/2405.12884/) (2024)
- [Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations](https://mlsanity.com/paper/2402.04442/) (2024)

## Abstract (arXiv)

In this paper, we share our best performing submission to the Arabic AI Tasks Evaluation Challenge (ArAIEval) at ArabicNLP 2023. Our focus was on Task 1, which involves identifying persuasion techniques in excerpts from tweets and news articles. The persuasion technique in Arabic texts was detected using a training loop with XLM-RoBERTa, a language-agnostic text representation model. This approach proved to be potent, leveraging fine-tuning of a multilingual language model. In our evaluation of the test set, we achieved a micro F1 score of 0.64 for subtask A of the competition.