🧑‍🔬 Hassan Sajjad — all works

29 works · ← Hassan Sajjad

1. What do Neural Machine Translation Models Learn about Morphology?Annual Meeting of the Association for Computational Linguistics (ACL)Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, et al.2017 · 487 citations
2. Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging TasksarXiv:1801.07772 · IJCNLP 8 (2017), volume 1, 1-10Yonatan Belinkov, Lluis Marquez, Hassan Sajjad, et al.2017 · 225 citations
3. Compressing Large-Scale Transformer-Based Models: A Case Study on BERTTransactions of the Association for Computational Linguistics (TACL)Prakhar Ganesh, Yao Chen, Xin Lou, et al.2021 · 190 citations
4. Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society ↗Findings of the Association for Computational Linguistics: EMNLP 2021 · no summary yetFiroj Alam et al.2021 · 182 citations
5. The AMARA Corpus: Building Parallel Language Resources for the Educational DomainLanguage Resources and Evaluation Conference (LREC)Ahmed Abdelali, Francisco Guzman, Hassan Sajjad, et al.2014 · 166 citations
6. Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine TranslationConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Fahim Dalvi, Nadir Durrani, Hassan Sajjad, et al.2018 · 148 citations
7. Identifying and Controlling Important Neurons in Neural Machine Translation ↗arXiv:1811.01157 · no summary yetAnthony Bau et al.2018 · 121 citations
8. Analyzing Individual Neurons in Pre-trained Language Models ↗Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yetNadir Durrani et al.2020 · 111 citations
9. Analyzing Redundancy in Pretrained Transformer Models ↗Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yetFahim Dalvi et al.2020 · 102 citations
10. Verifiably Effective Arabic Dialect IdentificationConference on Empirical Methods in Natural Language Processing (EMNLP)Kareem Darwish, Hassan Sajjad, Hamdy Mubarak2014 · 98 citations
11. Findings of the First Shared Task on Machine Translation Robustness ↗Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1) · no summary yetXian Li et al.2019 · 94 citations
12. Similarity Analysis of Contextual Word Representation Models ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetJohn Wu et al.2020 · 87 citations
13. Neuron-level Interpretation of Deep NLP Models: A SurveyTransactions of the Association for Computational Linguistics (TACL)Hassan Sajjad, Nadir Durrani, Fahim Dalvi2022 · 85 citations
14. AraBench: Benchmarking Dialectal Arabic-English Machine Translation ↗Proceedings of the 28th International Conference on Computational Linguistics · no summary yetHassan Sajjad et al.2020 · 69 citations
15. Highly Effective Arabic Diacritization using Sequence to Sequence ModelingConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Hamdy Mubarak, Ahmed Abdelali, Hassan Sajjad, et al.2019 · 51 citations
16. Findings of the WMT 2020 Shared Task on Machine Translation Robustness ↗Proceedings of the Fifth Conference on Machine Translation · no summary yetLucia Specia et al.2020 · 39 citations
17. QCRI @ DSL 2016: Spoken Arabic Dialect Identification Using Textual FeaturesInternational Conference on Computational Linguistics (COLING)Mohamed Eldesouki, Fahim Dalvi, Hassan Sajjad, et al.2016 · 32 citations
18. Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech TaggingHassan Sajjad, Fahim Dalvi, Nadir Durrani, et al.Semitic languages · 26 citations
19. How do Humans Evaluate Machine Translation ↗Proceedings of the Tenth Workshop on Statistical Machine Translation · no summary yetFrancisco Guzmán et al.2015 · 22 citations
20. A System for Diacritizing Four Varieties of ArabicConference on Empirical Methods in Natural Language Processing (EMNLP)Hamdy Mubarak, Ahmed Abdelali, Kareem Darwish, et al.2019 · 17 citations
21. QCMUQ@QALB-2015 Shared Task: Combining Character level MT and Error-tolerant Finite-State Recognition for Arabic Spelling CorrectionWorkshop on Arabic Natural Language ProcessingHouda Bouamor, Hassan Sajjad, Nadir Durrani, et al.2015 · 16 citations
22. AbjadGenEval: Abjad AI Generated Text Detection Shared Task for Languages Using Arabic Script at AbjadNLP 2026Workshop on NLP for Languages Using Arabic ScriptSaad Ezzini, Irfan Ahmad, Salmane Chafik, et al.2026 · 10 citations
23. Post-hoc analysis of Arabic transformer modelsBlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLPAhmed Abdelali, Nadir Durrani, Fahim Dalvi, et al.2022 · 8 citations
24. Eyes Don't Lie: Predicting Machine Translation Quality Using Eye Movement ↗Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yetHassan Sajjad et al.2016 · 8 citations
25. Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015 ↗arXiv:1606.05759 · no summary yetHassan Sajjad et al.2016 · 5 citations
26. Multilingual Nonce Dependency Treebanks: Understanding how Language Models Represent and Process Syntactic StructureConference of the North American Chapter of the Association for Computational Linguistics (NAACL)David Arps, Laura Kallmeyer, Younes Samih, et al.2024 · 4 citations
27. UrBLiMP: A Benchmark for Evaluating the Linguistic Competence of Large Language Models in UrduFindings of the Association for Computational LinguisticsFarah Adeeba, Brian Dillon, Hassan Sajjad, et al.2026 · 2 citations
28. AbjadStyleTransfer: Authorship Style Transfer for Arabic-Script Languages at AbjadNLP 2026 ↗Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script · no summary yetShadi Abudalfa et al.2026 · 1 citation
29. ZIP: Quantifying Which Words Matter in Zero-Shot Instructional PromptsJoint Conference on Lexical and Computational Semantics (*SEM)Nikta Gohari Sadr, Sangmitra Madhusudan, Arash Asgari, et al.2026

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