This paper describes our participation in the ArAIEval Shared Task 2024, focusing on Task 2C, which challenges participants to detect propagandistic elements in multimodal Arabic memes. The challenge involves analyzing both the textual and visual components of memes to identify underlying propagandistic messages. Our approach integrates the capabilities of MARBERT and ResNet50, top-performing pre-trained models for text and image processing, respectively. Our system architecture combines these models through a fusion layer that integrates and processes the extracted features, creating a comprehensive representation that is more effective in detecting nuanced propaganda. Our proposed system achieved significant success, placing second with an F1 score of 0.7987.

Semantic search tasks have grown extremely fast following the advancements in large language models, including the Reverse Dictionary and Word Sense Disambiguation in Arabic. This paper describes our participation in the Contemporary Arabic Dictionary Shared Task. We propose two models that achieved first place in both tasks. We conducted comprehensive experiments on the latest five multilingual sentence transformers and the Arabic BERT model for semantic embedding extraction. We achieved a ranking score of 0.06 for the reverse dictionary task, which is double than last year’s winner. We had an accuracy score of 0.268 for the Word Sense Disambiguation task.

DA-MSA Machine Translation is a recentchallenge due to the multitude of Arabic dialects and their variations. In this paper, we present our results within the context of Subtask 3 of the NADI-2024 Shared Task(Abdul-Mageed et al., 2024) that is DA-MSA Machine Translation . We utilized the DIALECTS008MSA MADAR corpus (Bouamor et al., 2018),the Emi-NADI corpus for the Emirati dialect (Khered et al., 2023), and we augmented thePalestinian and Jordanian datasets based onNADI 2021. Our approach involves develop013ing sentence-level machine translations fromPalestinian, Jordanian, Emirati, and Egyptiandialects to Modern Standard Arabic (MSA).To016 address this challenge, we fine-tuned models such as (Nagoudi et al., 2022)AraT5v2-msa-small, AraT5v2-msa-base, and (Elmadanyet al., 2023)AraT5v2-base-1024 to comparetheir performance. Among these, the AraT5v2-base-1024 model achieved the best accuracy, with a BLEU score of 0.1650 on the develop023ment set and 0.1746 on the test set.

A reverse dictionary takes a descriptive phrase of a particular concept and returns words with definitions that align with that phrase. While many reverse dictionaries cater to languages such as English and are readily available online or have been developed by researchers, there is a notable lack of similar resources for the Arabic language. This paper describes our participation in the Arabic Reverse Dictionary shared task. Our proposed method consists of two main steps: First, we convert word definitions into multidimensional vectors. Then, we train these encoded vectors using the Semi-Decoder model for our target task. Our system secured 2nd place based on the Rank metric for both embeddings (Electra and Sgns).

The rapid proliferation of disinformation through social media has become one of the most dangerous means to deceive and influence people’s thoughts, viewpoints, or behaviors due to social media’s facilities, such as rapid access, lower cost, and ease of use. Disinformation can spread through social media in different ways, such as fake news stories, doctored images or videos, deceptive data, and even conspiracy theories, thus making detecting disinformation challenging. This paper is a part of participation in the ArAIEval competition that relates to disinformation detection. This work evaluated four models: MARBERT, the proposed ensemble model, and two tests over GPT-4 (zero-shot and Few-shot). GPT-4 achieved micro-F1 79.01% while the ensemble method obtained 76.83%. Despite no improvement in the micro-F1 score on the dev dataset using the ensemble approach, we still used it for the test dataset predictions. We believed that merging different classifiers might enhance the system’s prediction accuracy.