Nizar Habash · page 8
Papers
- Bridging the English-Arabic Medical Knowledge Gap: Targeted Low-Rank Adaptation via Causal Layer Selection (2026)
LLMs handle English medical questions much better than Arabic ones, and this is usually blamed on scarce Arabic training data. - Computational Benchmarks for Egyptian Arabic Child Directed Speech (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… - EDRAC: Benchmarking Arabic Dialect Reading Comprehension (2026)
Dialectal Arabic has far fewer resources than Modern Standard Arabic. Existing dialect reading-comprehension tests are almost all multiple-choice and built from translated or social-media text. EDRAC fills this gap with an open-ended, generative QA benchmark for five dialects: Egyptian, Moroccan, Emirati, Syrian and… - Parsing Arabic Dialects Revisited: New Benchmarks, Models, and Insights (2026)
The paper revisits dependency parsing of dialectal Arabic, a task with little progress since the mid-2000s. - YallaMorph: A Benchmark for Evaluating Arabic Morphological Generation in Large Language Models (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… - Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data (2025)
Maltese is a low-resource Semitic language written in Latin script. Its spelling and vocabulary have drifted far from Arabic, so Arabic NLP data cannot be fed to Maltese models directly. The authors test whether Arabic labelled data can still help Maltese models when it is first brought closer to Maltese. They… - Lemmatizing Dialectal Arabic with Sequence-to-Sequence Models (2025)
Dialectal Arabic is hard to lemmatize. Spelling is not standardized, diacritics are usually missing, and morphological analyzers exist for only a few dialects, often with uneven quality. Saeed and Habash treat lemmatization as character-level generation with Seq2Seq models that output diacritized lemmas. They test… - EMAD: A Bridge Tagset for Unifying Arabic POS Annotations (2024)
Arabic has many part-of-speech tagsets, built by different groups for different goals. They disagree on what counts as a token, whether tags describe surface morphemes or underlying features, how much detail they encode, and how values are written. Because of this, existing annotated corpora are hard to merge, which… - AraSAS: The Open Source Arabic Semantic Tagger (2022)
Arabic is hard to tag semantically. Its rich morphology, attachable clitics and usually omitted short vowels create heavy ambiguity before ordinary polysemy is even considered. Open tools for coarse word-sense annotation were also missing. The authors present AraSAS, which they call the first open-source Arabic… - INVITED TALK 1: Computational Processing of Arabic Dialects (2014)
This item is the one-page abstract of an invited talk, not a full research paper. It reports no experiments, datasets, metrics or results. The talk addresses the processing of Arabic. Arabic is described as a family of varieties. Modern Standard Arabic (MSA) is the official written norm used in news, education and…
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