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Haitham Seelawi

Haitham Seelawi has 6 papers in our library, in 2019–2021, most of them on Semitic languages; often with Wael Farhan, Ahmad Mustafa, Hesham Al-Bataineh.

6 papers in our library · 2019–2021

OpenAlex · h-index 6 · 9 works · 151 citations OpenAlex, 2026-10-14

📄 6 works in our library · 🔗 Cited 94 times in our library · ⭐ Paper Stars ranking: #5835

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

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 6 worksArtificial Intelligence · 6 worksArts and Humanities · 5 worksLanguage and Linguistics · 5 worksInformation Systems · 2 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 6 works

 Works by year

2020s

20211 paper20201 paper

2010s

20194 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Multi-dialect Arabic BERT for Country-level Dialect Identification (2020 · 77 citations)
    The paper describes the system by team Mawdoo3 AI that took first place in subtask 1 of the NADI shared task.
  2. ALUE: Arabic Language Understanding Evaluation (2021 · 48 citations)
    Arabic NLP had no shared, GLUE-style benchmark for comparing general-purpose language understanding models, especially multi-task ones.
  3. Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning (2019 · 23 citations)
    The paper describes Mawdoo3 AI's entry to sub-task 1 of the MADAR shared task (WANLP 2019). The task is fine-grained Arabic dialect identification: each sentence from the travel domain is assigned to one of 26 classes, which are 25 city dialects plus MSA. The team represents each sentence with TF–IDF vectors over…
  4. Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions (2019 · 16 citations)
    The paper addresses question-to-question (Q2Q) semantic similarity for Arabic. Two questions count as equivalent when they would receive the same answer. The task is hard because Arabic has little labelled Q2Q data, a rich morphology, and many dialects that differ from Modern Standard Arabic (MSA).
  5. NSURL-2019 Task 8: Semantic Question Similarity in Arabic (2019 · 13 citations)
    This overview paper describes the shared task on Arabic question-to-question (Q2Q) semantic similarity, held at the NSURL 2019 workshop.
  6. NSURL-2019 Shared Task 8: Semantic Question Similarity in Arabic (2019 · 6 citations)
    This paper reports on Shared Task 8 of the NSURL 2019 workshop on under-resourced languages. Systems had to decide whether two Arabic questions mean the same thing, even when worded differently. The organizers define two questions as equivalent if they share the same answer; a partly different answer counts as…

 Co-authors here

12 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 94 times (of the 149,439 papers whose bibliographies we read); 66 citing papers with a page here →.

Updated

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