Nizar Habash · page 6
Papers
- Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic (2017 · 14 citations)
The paper addresses machine translation between Arabic dialects, specifically Egyptian to Levantine. This direction has almost no naturally written parallel text and had been evaluated only once before. The authors built a 12,000-sentence multi-dialect corpus. Translators started from English BTEC sentences rather… - Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injection (2025 · 13 citations)
Arabic automated essay scoring (AES) has little annotated training data. The main resource, ZAEBUC, has only 216 CEFR-rated essays. These are concentrated at B1–B2, with none at A1 or C2. The authors build a synthetic dataset to fill this gap in three phases: - CamelParser2.0: A State-of-the-Art Dependency Parser for Arabic (2023 · 13 citations)
An open-source Arabic parsing pipeline for both the CATiB and the UD formalisms. Raw text is normalised and stripped of diacritics, then a morphological disambiguator from CAMeL Tools (a BERT model or a lighter MLE one) gives tokens, lemmas, POS and features, including position-marked proclitics and enclitics and a… - MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction (2018 · 13 citations)
MADARi is a web-based tool for annotating social-media Arabic, which is heavily dialectal and full of spelling errors. - Exploiting Dialect Identification in Automatic Dialectal Text Normalization (2024 · 12 citations)
Written Dialectal Arabic has no standard spelling, so the same word can show up in many spontaneous forms. - OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields (2017 · 12 citations)
The paper describes an English system entered in every subtask of SemEval-2017 Task 4, which concerns Twitter sentiment. - Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings (2016 · 12 citations)
Automatic metrics for translation into morphologically rich languages handle three cases poorly: they cannot reliably separate wrong word choices, acceptable rephrasings, and inflection errors such as broken agreement or wrong case. - ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus (2024 · 11 citations)
Speech and language tools for Arabic work best on the formal standard variety. They perform poorly on regional dialects and on conversations where speakers mix languages or varieties, and evaluation data for such speech is scarce. The authors address this gap with ZAEBUC-Spoken, a spoken extension of the earlier… - A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions (2025 · 10 citations)
Code-switching (CSW) is common in the Arab world, yet language technology rarely handles it well. Speakers move between Modern Standard Arabic (MSA) and dialects, which the authors call diglossic CSW. They also mix Arabic with English, French and other languages, which is bilingual CSW. The authors collected papers… - Arabic Diacritics in the Wild: Exploiting Opportunities for Improved Diacritization (2024 · 10 citations)
Arabic is usually written without most of its optional diacritical marks, which makes the script highly ambiguous for NLP. - Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition (2013 · 10 citations)
Arabic OCR is hard because of connected letter shapes, diacritics and rich morphology. The BBN Byblos system recognises characters with an HMM and relies on an n-gram LM for fluency. The authors keep the decoder unchanged and instead rescore its n-best output. Each hypothesis receives features drawn from five families: - DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models (2026 · 9 citations)
Most Arabic LLM benchmarks test Modern Standard Arabic (MSA), but everyday Arabic is spoken and written in regional dialects that differ in lexicon, morphology and syntax. - Exploring the Impact of Transliteration on NLP Performance: Treating Maltese as an Arabic Dialect (2023 · 9 citations)
Maltese is a low-resource Semitic language. Its grammar is largely Arabic, but it is written in Latin script and has absorbed many Italian and English words. Script mismatch is known to limit how well multilingual models transfer to related languages. The authors therefore test whether rewriting Maltese in Arabic… - AraHealthQA 2025: The First Shared Task on Arabic Health Question Answering (2025 · 8 citations)
AraHealthQA 2025 is a shared task held with ArabicNLP 2025 at EMNLP 2025. It addresses the shortage of Arabic medical question-answering resources and standardized ways to compare systems. It has two tracks: - DIRA: Dialectal Arabic Information Retrieval Assistant (2013 · 8 citations)
Searching Arabic text is difficult for two reasons. The language has rich morphology, and dialects such as Egyptian Arabic (EGY) differ from Modern Standard Arabic (MSA) in vocabulary, inflection and spelling. These differences matter more now that social media carries a lot of informal, unedited dialectal writing.… - Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic (2024 · 7 citations)
The paper presents an open-source analyzer and generator for Modern Standard Arabic, built in the CAMeL Morph framework. - Cross-Lingual Transfer from Related Languages: Treating Low-Resource Maltese as Multilingual Code-Switching (2024 · 7 citations)
Multilingual encoders transfer poorly to a language whose script differs from that of its related high-resource languages. - Maknuune: A Large Open Palestinian Arabic Lexicon (2022 · 7 citations)
Palestinian Arabic (PAL), a South Levantine dialect, is widely spoken and increasingly written online, but it has no orthographic standard and few machine-readable resources. - An Online Readability Leveled Arabic Thesaurus (2020 · 7 citations)
Arabic is hard for learners. It has rich morphology, and words are usually written without short vowels, so a single written form can map to several unrelated lemmas. Few dictionaries in any language mark how difficult a word is, and none in Arabic did so before this work. The authors built a web thesaurus that… - Automatic Romanization of Arabic Bibliographic Records (2021 · 6 citations)
Library standards ask cataloguers to add Romanized versions of records in non-Latin scripts, and this is done by hand. - Identification of Naturally Occurring Numerical Expressions in Arabic (2008 · 6 citations)
The paper tackles Number Identification (NUMID) in running text. The task has two parts: finding the span of a numerical expression and converting it to a normalized digit-based value. Earlier work mostly converted isolated number words to digits, did so out of context, and broke on small format variations. Arabic… - MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmark (2026 · 5 citations)
Arabic has few openly available medical NLP resources, so it is hard to measure how well LLMs handle clinical content in this language. - The User-Aware Arabic Gender Rewriter (2023 · 5 citations)
Translation and other NLP tools usually return a single rendering of a sentence. In Arabic, nouns, adjectives and verbs carry grammatical gender, so such tools quietly choose genders for the speaker and the addressee, often along stereotyped lines such as a male doctor and a female nurse. The authors built a free web… - NLP for Arabic and Related Languages (2017 · 5 citations)
This is the editors' preface to a 2017 special issue of the TAL journal on Arabic NLP. It reports no experiments of its own. It first explains why Arabic is hard for language technology. The language has heavy morphology and high ambiguity, and ambiguity grows because short vowels are usually left out in writing.… - Do Diacritics Matter? Evaluating the Impact of Arabic Diacritics on Tokenization and LLM Benchmarks (2026 · 4 citations)
The paper asks how Arabic diacritics (vowel marks and related signs) change tokenization and the scores of large language models.
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