--- title: "Salam Khalifa — papers, page 2 · New York University Abu Dhabi" description: "Salam Khalifa, New York University Abu Dhabi: 28 papers on Elamur Research (2015–2026), h-index 14 — affiliations, topics, co-authors and…" canonical: "https://research.elamur.ai/free/en/authors/salam-khalifa/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/salam-khalifa/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Salam Khalifa](https://md.research.elamur.ai/free/en/authors/salam-khalifa/) / page 2 # Salam Khalifa · page 2 28 papers in our library · 2015–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/salam-khalifa/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/salam-khalifa/) [1](https://md.research.elamur.ai/free/en/authors/salam-khalifa/) **2** 26. **[Computational Benchmarks for Egyptian Arabic Child Directed Speech](https://md.research.elamur.ai/free/en/papers/khalifa-2026-computational-benchmarks-egyptian-arabic-child/)** *(2026)* ARABABYTALK-EGY makes Arabic child-adult interaction data usable for NLP. The Egyptian Arabic CHILDES corpus (EGYCHILDES) existed only as near-phonemic IPA transcriptions. Arabic NLP tools need orthographic input, so the corpus could not be used even for basic processing. The authors enriched it with three layers… 27. **[Learning Stress in Arabic Low-Resource Settings](https://md.research.elamur.ai/free/en/papers/qaddoumi-2026-learning-stress-arabic-low-resource/)** *(2026)* The paper addresses automatic prediction of lexical stress in spoken Arabic varieties, which have far fewer resources than Modern Standard Arabic. 28. **[YallaMorph: A Benchmark for Evaluating Arabic Morphological Generation in Large Language Models](https://md.research.elamur.ai/free/en/papers/reda-2026-yallamorph-benchmark-evaluating-arabic-morphological/)** *(2026)* YallaMorph is a benchmark of over 600,000 test instances for controlled Arabic morphological generation. The task gives a lemma, a part of speech, a gloss and a feature bundle, and the model must produce the surface form, or say that the configuration is invalid. Items cover verbs (active and passive, perfective and… [← prev](https://md.research.elamur.ai/free/en/authors/salam-khalifa/) [1](https://md.research.elamur.ai/free/en/authors/salam-khalifa/) **2**