Basem Ahmed


AraBEM at WANLP 2022 Shared Task: Propaganda Detection in Arabic Tweets
Eshrag Ali Refaee | Basem Ahmed | Motaz Saad
Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)

Propaganda is information or ideas that an organized group or government spreads to influence peopleś opinions, especially by not giving all the facts or secretly emphasizing only one way of looking at the points. The ability to automatically detect propaganda-related linguistic signs is a challenging task that researchers in the NLP community have recently started to address. This paper presents the participation of our team AraBEM in the propaganda detection shared task on Arabic tweets. Our system utilized a pre-trained BERT model to perform multi-class binary classification. It attained the best score at 0.602 micro-f1, ranking third on subtask-1, which identifies the propaganda techniques as a multilabel classification problem with a baseline of 0.079.

QQATeam at Qur’an QA 2022: Fine-Tunning Arabic QA Models for Qur’an QA Task
Basem Ahmed | Motaz Saad | Eshrag A. Refaee
Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection

The problem of auto-extraction of reliable answers from a reference text like a constitution or holy book is a real challenge for the natural languages research community. Qurán is the holy book of Islam and the primary source of legislation for millions of Muslims around the world, which can trigger the curiosity of non-Muslims to find answers about various topics from the Qurán. Previous work on Question Answering (Q&A) from Qurán is scarce and lacks the benchmark of previously developed systems on a testbed to allow meaningful comparison and identify developments and challenges. This work presents an empirical investigation of our participation in the Qurán QA shared task (2022) that utilizes a benchmark dataset of 1,093 tuples of question-Qurán passage pairs. The dataset comprises Qurán verses, questions and several ranked possible answers. This paper describes the approach we follow with our participation in the shared task and summarises our main findings. Our system attained the best score at 0.63 pRR and 0.59 F1 on the development set and 0.56 pRR and 0.51 F1 on the test set. The best results of the Exact Match (EM) score at 0.34 indicate the difficulty of the task and the need for more future work to tackle this challenging task.