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Salmane Chafik

Salmane Chafik has 6 papers in our library, in 2025–2026, most of them on Semitic languages, Sentiment & emotion, Language models & pretraining; often with Saad Ezzini, Hamzah Luqman, Mo El-Haj.

6 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 3 · 15 works · 16 citations OpenAlex, 2026-10-14

📄 6 works in our library · 🔗 Cited 63 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7350

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. The AraGenEval Shared Task on Arabic Authorship Style Transfer and AI Generated Text Detection (2025 · 36 citations)
    AraGenEval was a shared task at ArabicNLP 2025 (co-located with EMNLP 2025). It filled a gap: Arabic had no dedicated benchmark for imitating an author's style or for telling human writing from machine writing. The organizers defined three subtasks. The first, Authorship Style Transfer, rewrites a formal Arabic…
  2. AHaSIS: Shared Task on Sentiment Analysis for Arabic Dialects (2025 · 15 citations)
    The AHaSIS 2025 shared task addresses a gap in Arabic sentiment analysis. Most labelled resources cover Modern Standard Arabic (MSA), and models trained on them transfer poorly to regional dialects. The organisers took MSA hotel reviews from the SemEval-2016 ABSA-Hotels data, collected from Booking.com and…
  3. AbjadGenEval: Abjad AI Generated Text Detection Shared Task for Languages Using Arabic Script at AbjadNLP 2026 (2026 · 10 citations)
    AbjadGenEval is a shared task held at the AbjadNLP workshop (EACL 2026). It tests whether systems can tell human-written news from LLM-generated news in languages written in Arabic script. The organizers argue that existing detectors were built mostly for English and break down on Arabic-script text, where rich…
  4. AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP (2025 · 8 citations)
    AraReasoner asks how well recent reasoning-oriented LLMs handle Arabic. Arabic is hard for models because of its rich morphology, many dialects, and non-Latin script, and earlier Arabic benchmarks mostly covered BERT-style encoders or only a few closed GPT models. The authors assembled fifteen public Arabic datasets…
  5. AraSentEval 2026: A Shared Task on Sentiment Analysis and Swapping in Arabic (2026)
    AraSentEval is a shared task held at OSACT7 (LREC 2026). It targets two weak spots in Arabic sentiment work. The first is that most benchmarks cover MSA or only a few dialects. The second is that controllable sentiment generation is barely studied. The organisers set up two subtasks:
  6. OBSIDIAN: An OSINT-Driven NLP Framework for Detecting Cyber-Physical Threats in Arabic Social Media (2026)
    The paper targets a gap in Arabic social-media NLP. Prior datasets mostly cover hate speech, offensive language, cyberbullying and harassment, and they treat harm as linguistic toxicity. Security-relevant signals that analysts need for early warning, such as violent intent, explicit threats or people asking for help…

All works

7 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

  1. Dialect2SQL: A Novel Text-to-SQL Dataset for Arabic Dialects with a Focus on Moroccan Darija ↗Proceedings of the 4th Workshop on Arabic Corpus Linguistics (WACL-4) · no summary yetSalmane Chafik, Saad Ezzini, Ismail Berrada2025

 Co-authors here

12 of 21 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 63 times (of the 149,439 papers whose bibliographies we read); 40 citing papers with a page here →.

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