📰 WANLP 2021 — accepted papers

WANLP 2021 is the 2021 edition of the Arabic Natural Language Processing Workshop (WANLP), held in Kyiv, Ukraine (Virtual), April 2021 (publisher: Association for Computational Linguistics). The ACL Anthology lists 53 papers of it; our library holds 23 with our summaries. Below: the most cited papers, every accepted paper, and ours with their topics, authors and institutions.

All accepted papers (53)

The most cited first; 23 with our summary link to their page here, the others to the original.

1. A Contextual Word Embedding for Arabic Sarcasm Detection with Random ForestsHazem Elgabry, Shimaa Attia, Ahmed Abdel-Rahman et al.our summary
2. ALUE: Arabic Language Understanding EvaluationHaitham Seelawi, Ibraheem Tuffaha, Mahmoud Gzawi et al.
3. Adapting MARBERT for Improved Arabic Dialect Identification: Submission to the NADI 2021 Shared TaskBadr AlKhamissi, Mohamed Gabr, Muhammad ElNokrashy et al.
4. Adult Content Detection on Arabic Twitter: Analysis and ExperimentsHamdy Mubarak, Sabit Hassan, Ahmed Abdelali
5. ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation NetworksFatima Haouari, Maram Hasanain, Reem Suwaileh et al.
6. ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation DetectionFatima Haouari, Maram Hasanain, Reem Suwaileh et al.
7. ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic TweetsLaila Bashmal, Daliyah AlZeerour summary
8. AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic TweetsAnshul Wadhawanour summary
9. AraELECTRA: Pre-Training Text Discriminators for Arabic Language UnderstandingWissam Antoun, Fady Baly, Hazem Hajjour summary
10. AraFacts: The First Large Arabic Dataset of Naturally Occurring ClaimsZien Sheikh Ali, Watheq Mansour, Tamer Elsayed et al.
11. AraGPT2: Pre-Trained Transformer for Arabic Language GenerationWissam Antoun, Fady Baly, Hazem Hajjour summary
12. Arabic Compact Language Modelling for Resource Limited DevicesZaid Alyafeai, Irfan Ahmad
13. Arabic Dialect Identification based on a Weighted Concatenation of TF-IDF FeaturesMohamed Lichouri, Mourad Abbas, Khaled Lounnas et al.
14. Arabic Emoji Sentiment Lexicon (Arab-ESL): A Comparison between Arabic and European Emoji Sentiment LexiconsShatha Ali A. Hakami, Robert Hendley, Phillip Smith
15. Arabic Offensive Language on Twitter: Analysis and ExperimentsHamdy Mubarak, Ammar Rashed, Kareem Darwish et al.our summary
16. Automatic Difficulty Classification of Arabic SentencesNouran Khallaf, Serge Sharoff
17. Automatic Romanization of Arabic Bibliographic RecordsFadhl Eryani, Nizar Habash
18. BERT-based Multi-Task Model for Country and Province Level MSA and Dialectal Arabic IdentificationAbdellah El Mekki, Abdelkader El Mahdaouy, Kabil Essefar et al.
19. Benchmarking Transformer-based Language Models for Arabic Sentiment and Sarcasm DetectionIbrahim Abu Farha, Walid Magdyour summary
20. Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment IdentificationAmey Hengle, Atharva Kshirsagar, Shaily Desai et al.our summary
21. Country-level Arabic Dialect Identification Using Small Datasets with Integrated Machine Learning Techniques and Deep Learning ModelsMaha J. Althobaiti
22. Country-level Arabic Dialect Identification using RNNs with and without Linguistic FeaturesElsayed Issa, Mohammed AlShakhori, Reda Al-Bahrani et al.
23. Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic LanguageAbdelkader El Mahdaouy, Abdellah El Mekki, Kabil Essefar et al.our summary
24. DeepBlueAI at WANLP-EACL2021 task 2: A Deep Ensemble-based Method for Sarcasm and Sentiment Detection in ArabicBingyan Song, Chunguang Pan, Shengguang Wang et al.our summary
25. DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word EmbeddingsMuhammad Abdul-Mageed, Shady Elbassuoni, Jad Doughman et al.

See all 53 papers in WANLP 2021 — accepted papers →

📄 Papers with our summary in our library (23)

The most cited first (OpenAlex citations plus our library's).

1. Arabic Offensive Language on Twitter: Analysis and ExperimentsHamdy Mubarak, Ammar Rashed, Kareem Darwish, et al.2021 · 165 citations
2. Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in ArabicIbrahim Abu Farha, Wajdi Zaghouani, Walid Magdy2021 · 112 citations
3. AraELECTRA: Pre-Training Text Discriminators for Arabic Language UnderstandingWissam Antoun, Fady Baly, Hazem Hajj2021 · 97 citations
4. AraGPT2: Pre-Trained Transformer for Arabic Language GenerationWissam Antoun, Fady Baly, Hazem Hajj2021 · 79 citations
5. NADI 2021: The Second Nuanced Arabic Dialect Identification Shared TaskMuhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, et al.2021 · 67 citations
6. Benchmarking Transformer-based Language Models for Arabic Sentiment and Sarcasm DetectionIbrahim Abu Farha, Walid Magdy2021 · 64 citations
7. Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data AugmentationAbeer Abuzayed, Hend Al-Khalifa2021 · 36 citations
8. Machine Learning-Based Model for Sentiment and Sarcasm DetectionHamada Nayel, Eslam Amer, Aya Allam, et al.2021 · 25 citations
9. Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic LanguageAbdelkader El Mahdaouy, Abdellah El Mekki, Kabil Essefar, et al.2021 · 22 citations
10. Leveraging Offensive Language for Sarcasm and Sentiment Detection in ArabicFatemah Husain, Ozlem Uzuner2021 · 19 citations
11. SarcasmDet at Sarcasm Detection Task 2021 in Arabic using AraBERT Pretrained ModelDalya Faraj, Malak Abdullah2021 · 18 citations
12. A Contextual Word Embedding for Arabic Sarcasm Detection with Random ForestsHazem Elgabry, Shimaa Attia, Ahmed Abdel-Rahman, et al.2021 · 16 citations
13. Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment AnalysisAbdullah I. Alharbi, Mark Lee2021 · 14 citations
14. Preprocessing Solutions for Detection of Sarcasm and Sentiment for ArabicMohamed Lichouri, Mourad Abbas, Besma Benaziz, et al.2021 · 13 citations
15. AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic TweetsAnshul Wadhawan2021 · 11 citations
16. Sarcasm and Sentiment Detection in Arabic language A Hybrid Approach Combining Embeddings and Rule-based FeaturesKamel Gaanoun, Imade Benelallam2021 · 10 citations
17. The IDC System for Sentiment Classification and Sarcasm Detection in ArabicAbraham Israeli, Yotam Nahum, Shai Fine, et al.2021 · 10 citations
18. ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic TweetsLaila Bashmal, Daliyah AlZeer2021 · 8 citations
19. iCompass at Shared Task on Sarcasm and Sentiment Detection in ArabicMalek Naski, Abir Messaoudi, Hatem Haddad, et al.2021 · 7 citations
20. Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment IdentificationAmey Hengle, Atharva Kshirsagar, Shaily Desai, et al.2021 · 6 citations
21. WANLP 2021 Shared-Task: Towards Irony and Sentiment Detection in Arabic Tweets using Multi-headed-LSTM-CNN-GRU and MaRBERTReem Abdel-Salam2021 · 5 citations
22. DeepBlueAI at WANLP-EACL2021 task 2: A Deep Ensemble-based Method for Sarcasm and Sentiment Detection in ArabicBingyan Song, Chunguang Pan, Shengguang Wang, et al.2021 · 5 citations
23. Sarcasm and Sentiment Detection in Arabic: investigating the interest of character-level featuresDhaou Ghoul, Gaël Lejeune2021 · 2 citations

🧭 Top topics

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

Semitic languages (23)😊Sentiment & emotion (19)🧠Language models & pretraining (1)✍️Text generation (1)🌍Multilingual & cross-lingual NLP (1)🧩Morphology (1)📏Evaluation & benchmarks (1)

📚 Fields

🖥️Computer Science (22)🎭Arts and Humanities (20)💭Psychology (1)🤝Social Sciences (1)

🧑‍🔬 Top authors

Authors with a page who published here most often: their works here, and the citations of those works.

🏛️ Top institutions

Institutions on the bylines of its works here, by the number of works.

🎓American University of BeirutBeirut · Lebanon2 works here

Place and dates from the event's site or the ACL Anthology; works and citations from OpenAlex and our library.

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