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Baraah Qawasmeh

Baraah Qawasmeh has 2 papers in our library, in 2025–2026, most of them on Semitic languages, Machine translation, Vision & language; often with Abdellah El Mekki, Houdaifa Atou, Omer Nacar.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 6 · 18 works · 100 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 12 times in our library · ⭐ Paper Stars ranking: #9444

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

🎓Western Michigan UniversityKalamazoo · United States2022–2025 · 12 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSocial Sciences · 1 workComputer Vision and Pattern Recognition · 1 workEducation · 1 work

🧭 Topics 3Research subjects and languages the work is about.Semitic languages · 2 worksVision & language · 1 workMachine translation · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset (2025 · 11 citations)
    Large vision-language models (LVLMs) tend to reflect Western viewpoints. Existing cultural VQA resources often rely on translated material, cover few images or topics, ask shallow factual questions, and treat a culture as uniform. The authors built Pearl to address these gaps. It is a large Arabic multimodal…
  2. Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs (2026 · 4 citations)
    In day-to-day life, Arabic speakers mostly use their local dialects instead of Modern Standard Arabic. MT systems trained mainly on MSA or English-centric data therefore handle dialectal input poorly. Earlier parallel resources (PADIC, MADAR, FLORES+) have limits: narrow domains, short sentences, or dialect entries…

 Co-authors here

12 of 13 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 12 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

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