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Marwan Torki

Marwan Torki has 19 papers in our library, in 2014–2025, most of them on Semitic languages, Question answering, Sentiment & emotion; often with Nagwa El-Makky, Heba Abdelnasser, Maha Ragab.

19 papers in our library · 2014–2025

OpenAlex · ORCID · h-index 22 · 151 works · 2,740 citations OpenAlex, 2026-10-14

📄 19 works in our library · 🔗 Cited 80 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5177

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. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

 Works by year

2020s

2010s

 Papers

The most cited first (OpenAlex and our library).

  1. Al-Bayan: An Arabic Question Answering System for the Holy Quran (2014 · 86 citations)
    Al-Bayan is a question answering system for Arabic questions about the Holy Quran. The authors argue that Arabic QA is still weak and that religious topics need trustworthy answers, since the web holds much unreliable content about them. The system draws only on the Quran and two classical interpretation books…
  2. Arabic Dialect Identification with Deep Learning and Hybrid Frequency Based Features (2019 · 25 citations)
    The paper addresses fine-grained Arabic dialect identification: assigning a sentence to one of 25 city dialects or Modern Standard Arabic (MSA).
  3. AraDepSu: Detecting Depression and Suicidal Ideation in Arabic Tweets Using Transformers (2022 · 24 citations)
    The paper addresses the lack of Arabic resources for spotting signs of depression and suicidal thoughts on social media.
  4. Al-Bayan: A Knowledge-based System for Arabic Answer Selection (2015 · 21 citations)
    The paper tackles answer selection for Arabic community QA in SemEval-2015 Task 3, Subtask A. Questions come from the Fatwa website on Islamic topics, and each candidate answer must be labeled Direct, Related or Irrelevant. The authors' system relies on a domain knowledge resource rather than generic text features.…
  5. AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify (2022 · 20 citations)
    The authors tackle the three-level Arabic hate speech shared task at OSACT5 2022, using tweets annotated by Mubarak et al.
  6. QU-IR at SemEval 2016 Task 3: Learning to Rank on Arabic Community Question Answering Forums with Word Embedding (2016 · 19 citations)
    This system paper addresses answer re-ranking for Arabic community question answering in SemEval 2016 Task 3, Subtask D.
  7. Arabic Dialect Identification Using BERT Fine-Tuning (2020 · 14 citations)
    The paper describes a submission to sub-task 1 of the NADI 2020 shared task, which asks a system to guess which of 21 Arab countries a tweet's dialect comes from.
  8. Identifying Nuanced Dialect for Arabic Tweets with Deep Learning and Reverse Translation Corpus Extension System (2020 · 9 citations)
    The paper describes the CodeLyoko team's entry to the NADI 2020 Shared Task, Subtask-1. The task is to guess which of 21 Arab countries a tweet's dialect comes from. Two things make it hard: the classes are heavily imbalanced, and neighbouring dialects share much vocabulary. To rebalance the data without any external…
  9. QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering Forums (2017 · 9 citations)
    The paper addresses SemEval 2017 Task 3, Subtask D, which concerns Arabic community question answering. A search engine retrieves 30 earlier question-answer pairs for each new question. The system must re-rank these pairs by relevance and label each one as relevant or irrelevant.
  10. AlexUNLP-BH at StanceEval2024: Multiple Contrastive Losses Ensemble Strategy with Multi-Task Learning For Stance Detection in Arabic (2024 · 8 citations)
    The paper describes the winning system of the StanceEval 2024 shared task at ArabicNLP 2024. Systems must assign each Arabic tweet a stance of Favor, Against or None with respect to one of three targets: women empowerment, the COVID-19 vaccine, or digital transformation. The authors use only the Mawqif dataset.…
  11. Arabic Dialect Identification with a Few Labeled Examples Using Generative Adversarial Networks (2022 · 6 citations)
    The paper addresses Arabic dialect identification when only a handful of labeled examples exist for each dialect.
  12. Alex-U 2023 NLP at WojoodNER shared task: AraBINDER (Bi-encoder for Arabic Named Entity Recognition) (2023 · 5 citations)
    The paper tackles Arabic named entity recognition for the WojoodNER 2023 shared task, which has a flat track and a nested track.
  13. AlexU-AIC at WojoodNER shared task: Sequence Labeling vs MRC and SWA for Arabic Named Entity Recognition (2023 · 5 citations)
    The paper describes an Alexandria University / Applied Innovation Center entry to the WojoodNER 2023 shared task on Arabic named entity recognition, which had a flat and a nested subtask.
  14. AlexUNLP-MZ at ArAIEval Shared Task: Contrastive Learning, LLM Features Extraction and Multi-Objective Optimization for Arabic Multi-Modal Meme Propaganda Detection (2024 · 4 citations)
    The paper describes the winning approach to the ArAIEval 2024 shared task on detecting propaganda in Arabic memes.
  15. AlexUNLP-STM at NADI 2024 shared task: Quantifying the Arabic Dialect Spectrum with Contrastive Learning, Weighted Sampling, and BERT-based Regression Ensemble (2024 · 3 citations)
    The paper describes a system for Subtask 2 of NADI 2024, which asks how dialectal an Arabic sentence is. The answer is a continuous Arabic Level of Dialectness (ALDi) score from 0 (pure Modern Standard Arabic) to 1 (strongly dialectal), not a binary MSA-vs-dialect label. The authors trained on AOC-ALDi, which is…
  16. AlexuNLP24 at AraFinNLP2024: Multi-Dialect Arabic Intent Detection with Contrastive Learning in Banking Domain (2024 · 3 citations)
    The paper tackles intent detection for Arabic banking queries when the evaluation data contains dialects absent from training.
  17. AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques (2022 · 3 citations)
    The paper describes the AlexU-AL team's entry to the Arabic track of SemEval-2022 Task 6 (iSarcasmEval). The team worked on two sub-tasks. Sub-task A is binary sarcasm classification of single tweets. Sub-task C asks which of two texts with the same meaning is the sarcastic one. The training data is small: 3,102…
  18. MA at AraFinNLP2024: BERT-based Ensemble for Cross-dialectal Arabic Intent Detection (2024 · 2 citations)
    The paper tackles banking intent detection across Arabic dialects for subtask-1 of the AraFinNLP 2024 shared task.
  19. Open-domain Arabic Conversational Question Answering with Question Rewriting (2025 · 1 citation)
    Arabic has had no open-domain conversational question answering (CQA) system. Two things were missing: a suitable dataset and a way to handle follow-up questions that depend on earlier turns. The authors built AraQReCC by machine-translating, with Googletrans, the QuAC-derived part of the English QReCC benchmark. The…

All works

20 works of theirs in our library, the most cited first; 19 with a page here, the others link their original until we summarise them.

  1. Arabic Dialect Identification Using BERT Fine-TuningArabic Natural Language Processing Workshop (WANLP)Moataz Mansour, Moustafa Tohamy, Zeyad Ezzat, et al.2020 · 14 citations
  2. Learning Word Embeddings from Glosses: A Multi-Loss Framework for Arabic Reverse Dictionary Tasks ↗Proceedings of The Third Arabic Natural Language Processing Conference · no summary yetEngy Ibrahim et al.2025

 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 80 times (of the 149,439 papers whose bibliographies we read); 56 citing papers with a page here →.

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