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This paper introduces seven synthetic datasets in dialects alongside Modern Standard Arabic (MSA), created using Machine Translation (MT) combined with human post-editing.
Dr. Firoz Alam
5 min read
The aim of this study was to measure how common timing and location confounders explain variation in sentiment on Twitter.
Dr. Zubair Shah
7 min read
This paper focuses on natural language processing techniques to analyze the semantic similarity in Hadiths, which are significant religious texts in Islam. AraVec and GPT embeddings are used to represent Hadiths numerically, followed by UMAP to project these embeddings to 2D.
Dr. Younss Ait Mou
6 min read
This paper focuses on detecting propagandistic spans and persuasion techniques in Arabic text from tweets and news paragraphs.
Md. Rafiul Biswas
8 min read
This paper is about the effects of prolonged exposure to hate speech on the mental and physical health of annotators, as well as researchers with work revolving around the topic of hate speech.
Mabrouka Bessghaier
5 min read
The main goal of this paper is to examine the possibilities and obstacles of combining automatic speech recognition with machine translation in a web-based audio-video environment, and in a real-time setting in the sports domain that covers football matches for the purpose of creating a multilingual dataset.
Dr. Wajdi Zaghouani
6 min read
