{"title":"Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space - Transformer Ensemble Models Tackling Deception and Persuasion","authors":"Sudeep Mangalvedhekar, Kshitij Deshpande, Yash Patwardhan, Vedant Deshpande, Ravindra Murumkar","doi":"10.48550/arXiv.2311.18730","DOIUrl":null,"url":null,"abstract":"In this paper, we highlight our approach for the “Arabic AI Tasks Evaluation (ArAiEval) Shared Task 2023”. We present our approaches for task 1-A and task 2-A of the shared task which focus on persuasion technique detection and disinformation detection respectively. Detection of persuasion techniques and disinformation has become imperative to avoid distortion of authentic information. The tasks use multigenre snippets of tweets and news articles for the given binary classification problem. We experiment with several transformer-based models that were pre-trained on the Arabic language. We fine-tune these state-of-the-art models on the provided dataset. Ensembling is employed to enhance the performance of the systems. We achieved a micro F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task 2-A (7th rank on the leaderboard) respectively.","PeriodicalId":503921,"journal":{"name":"ARABICNLP","volume":"14 1","pages":"513-518"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ARABICNLP","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2311.18730","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we highlight our approach for the “Arabic AI Tasks Evaluation (ArAiEval) Shared Task 2023”. We present our approaches for task 1-A and task 2-A of the shared task which focus on persuasion technique detection and disinformation detection respectively. Detection of persuasion techniques and disinformation has become imperative to avoid distortion of authentic information. The tasks use multigenre snippets of tweets and news articles for the given binary classification problem. We experiment with several transformer-based models that were pre-trained on the Arabic language. We fine-tune these state-of-the-art models on the provided dataset. Ensembling is employed to enhance the performance of the systems. We achieved a micro F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task 2-A (7th rank on the leaderboard) respectively.
本文重点介绍了我们针对 "阿拉伯语人工智能任务评估(ArAiEval)2023 共享任务 "所采用的方法。我们介绍了针对任务 1-A 和任务 2-A 的方法,这两个任务分别侧重于说服技术检测和虚假信息检测。为避免真实信息失真,检测劝诱技术和虚假信息已成为当务之急。这些任务使用推文和新闻文章的多源片段来解决给定的二元分类问题。我们试验了几种基于转换器的模型,这些模型已在阿拉伯语中进行了预先训练。我们在提供的数据集上对这些最先进的模型进行了微调。为了提高系统的性能,我们采用了集合的方法。我们在任务 1-A 和任务 2-A 上分别取得了 0.742(排行榜第 8 位)和 0.901(排行榜第 7 位)的微型 F1 分数。