توضح هذه الورقة وتبحث في أنظمة مختلفة لمعالجة المهمة 6 من Semeval-2021: اكتشاف تقنيات الإقناع في النصوص والصور، والتعقب الفرعي 1. تهدف المهمة إلى بناء نموذج لتحديد التقنيات الطبية والنفسية (مثل التبسيط المفاجئ، الاسم-Cling، تشويه) في المحتوى النصي من ميمي يستخدم غالبا في حملة تضليل للتأثير على المستخدمين.توفر الورقة مقارنة واسعة النطاق بين مختلف أنظمة تعليم الآلات كحل للمهمة.نقوم بتوصيل المعالجة المسبقة للبيانات النصية لصالح المهمة وعدة طرق للتغلب على خلل الفصل.تظهر النتائج أن ضبط نموذج روبرتا يعطى أفضل النتائج مع نقاط F1-Micro من 0.51 على مجموعة التطوير.
This paper describes and examines different systems to address Task 6 of SemEval-2021: Detection of Persuasion Techniques In Texts And Images, Subtask 1. The task aims to build a model for identifying rhetorical and psycho- logical techniques (such as causal oversimplification, name-calling, smear) in the textual content of a meme which is often used in a disinformation campaign to influence the users. The paper provides an extensive comparison among various machine learning systems as a solution to the task. We elaborate on the pre-processing of the text data in favor of the task and present ways to overcome the class imbalance. The results show that fine-tuning a RoBERTa model gave the best results with an F1-Micro score of 0.51 on the development set.
References used
https://aclanthology.org/
We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems. The task focused on memes and had three subtasks: (i) de
The following system description presents our approach to the detection of persuasion techniques in texts and images. The given task has been framed as a multi-label classification problem with the different techniques serving as class labels. The mu
The objective of subtask 2 of SemEval-2021 Task 6 is to identify techniques used together with the span(s) of text covered by each technique. This paper describes the system and model we developed for the task. We first propose a pipeline system to i
We developed a system for task 6 sub-task 1 for detecting propaganda in memes. An external dataset and augmentation data-set were used to extend the official competition data-set. Data augmentation techniques were applied on the external data-set and
We describe our approach for SemEval-2021 task 6 on detection of persuasion techniques in multimodal content (memes). Our system combines pretrained multimodal models (CLIP) and chained classifiers. Also, we propose to enrich the data by a data augmentation technique. Our submission achieves a rank of 8/16 in terms of F1-micro and 9/16 with F1-macro on the test set.