--- title: "AbdelRahim Elmadany — papers, page 2 — Elamur Research" description: "AbdelRahim Elmadany, University of British Columbia: 32 papers on Elamur Research (2015–2026), h-index 14 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/abdelrahim-elmadany/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/abdelrahim-elmadany/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [AbdelRahim Elmadany](https://md.research.elamur.ai/free/en/authors/abdelrahim-elmadany/) / page 2 # AbdelRahim Elmadany · page 2 32 papers in our library · 2015–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/abdelrahim-elmadany/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/abdelrahim-elmadany/) [1](https://md.research.elamur.ai/free/en/authors/abdelrahim-elmadany/) **2** 26. **[A Benchmark Study of Contrastive Learning for Arabic Social Meaning](https://md.research.elamur.ai/free/en/papers/khondaker-2022-benchmark-study-contrastive-learning-arabic/)** *(2022 · 7 citations)* The paper asks whether supervised contrastive learning (CL), which has helped many NLP tasks, also helps Arabic social meaning tasks such as dialect identification, emotion recognition, abusive and hate speech detection, and demographic attribute prediction. 27. **[Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology](https://md.research.elamur.ai/free/en/papers/sullivan-2026-arab-voices-mapping-standard-dialectal/)** *(2026 · 3 citations)* Speech resources for Dialectal Arabic (DA) are scattered and inconsistent. Corpora use different dialect labeling schemes (country names, broad regions, ISO codes), cover different domains, and are recorded under different conditions. This makes it hard to compare datasets or evaluate models fairly. The authors… 28. **[DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings](https://md.research.elamur.ai/free/en/papers/mageed-2021-dialex-benchmark-evaluating-multidialectal-arabic/)** *(2021 · 3 citations)* Arabic dialects had no shared way to check whether word embeddings capture their vocabulary and grammar. Earlier analogy benchmarks for Arabic covered only Modern Standard Arabic (MSA) or were translated from English. DiaLex fills this gap. It is a hand-built set of word pairs for Algerian, Egyptian, Lebanese, Syrian… 29. **[Turn Segmentation into Utterances for Arabic Spontaneous Dialogues and Instance Messages](https://md.research.elamur.ai/free/en/papers/elmadany-2015-turn-segmentation-utterances-arabic-spontaneous/)** *(2015 · 3 citations)* The paper addresses how to split a speaker's turn in Egyptian Arabic dialogues and instant messages (IM) into utterances, the smaller units that each carry one dialogue act. 30. **[AlexandriaX 2026: The First Shared Task on Dialectal Arabic Machine Translation](https://md.research.elamur.ai/free/en/papers/mekki-2026-alexandriax-2026-first-shared-task/)** *(2026 · 2 citations)* AlexandriaX 2026 is a shared task on machine translation for Arabic dialects. Most Arabic tools were built for MSA. Dialect output can keep the meaning and still sound wrong, for example through incorrect gender agreement, the wrong level of formality, or MSA slipping in. Aggregate metrics also say little about why… 31. **[Bulbul: A Dataset for Dialectal Arabic Speech Recognition](https://md.research.elamur.ai/free/en/papers/ashraf-2026-bulbul-dataset-dialectal-arabic-speech/)** *(2026 · 1 citation)* Arabic speech recognition struggles because speech resources are scarce and skewed toward a few varieties: MSA, Egyptian, broad Levantine, Gulf and Moroccan. 32. **[NADI 2026: The Second Multidialectal Arabic Speech Processing Shared Task](https://md.research.elamur.ai/free/en/papers/sullivan-2026-nadi-2026-second-multidialectal-arabic/)** *(2026)* NADI 2026 is the seventh round of the NADI series and the second focused on spoken dialectal Arabic. It targets a mismatch: benchmarks tend to use clean, in-domain recordings of a few varieties, while real systems face poor audio, speakers whose variety is not known, mid-utterance language switching and unfamiliar… [← prev](https://md.research.elamur.ai/free/en/authors/abdelrahim-elmadany/) [1](https://md.research.elamur.ai/free/en/authors/abdelrahim-elmadany/) **2**