---
title: "AraREQ — paper by Hammouda et al. (2026)"
description: "The authors build AraREQ by Arabizing four English requirement-pair collections (PURE, WorldVista, OPENCOSS, UAV). 41 references."
authors: ["Tymaa Hasanain Hammouda", "Alaa Aljabari", "Nagham Fahim Hamad", "Mustafa Jarrar"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.553/"
doi: "10.63317/357spa5iu5zq"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hammouda-2026-arareq-dataset-end-end-system/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/hammouda-2026-arareq-dataset-end-end-system/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/4/) / AraREQ: A Dataset and End-to-End System for Conflict Detection and Resolution in Software Requirements

# AraREQ: A Dataset and End-to-End System for Conflict Detection and Resolution in Software Requirements

**Authors:** [Tymaa Hasanain Hammouda](https://md.research.elamur.ai/free/en/authors/tymaa-hammouda/), Alaa Aljabari, [Nagham Fahim Hamad](https://md.research.elamur.ai/free/en/authors/nagham-hamad/) and [Mustafa Jarrar](https://md.research.elamur.ai/free/en/authors/mustafa-jarrar/) · **Year:** 2026 · **Venue:** [Proceedings of the Fifteenth Language Resources and Evaluation Conference](https://md.research.elamur.ai/free/en/venues/lrec/) ([LREC 2026](https://md.research.elamur.ai/free/en/venues/lrec/2026/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.553/) · [PDF](https://aclanthology.org/2026.lrec-1.553.pdf) · [DOI 10.63317/357spa5iu5zq](https://doi.org/10.63317/357spa5iu5zq) · [OpenAlex](https://openalex.org/W7160129942)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Requirement documents for software often contain pairs of statements that contradict each other. Tools for catching such clashes exist mostly for English, and Arabic has had no annotated data for the task. The authors build AraREQ by Arabizing four English requirement-pair collections (PURE, WorldVista, OPENCOSS, UAV). GPT-4o produces draft translations, and a linguist and a software engineer jointly correct them. The source data contains very few conflict pairs, so the authors add synthetic conflicts. They create these by editing requirements with hand-defined patterns such as negation, changed numbers and altered conditions, then have an LLM reword the edits so the contradiction is less obvious. The result is about 27K labelled pairs. Four LLMs are benchmarked with zero-shot and few-shot prompting, and few-shot prompting helps most on the rare conflict class. The authors also release an open end-to-end tool that takes a CSV of requirements, flags likely conflicts and proposes rewrites. This gives Arabic requirements engineering its first sizeable public benchmark and a working baseline system.

## Key points
- **Source data:** 448 unique English requirements forming 26,545 pairs, of which only 83 were labelled conflict.
- **After augmentation:** AraREQ has 26,462 neutral and 547 conflict pairs. The authors describe it as the largest public Arabic resource of its kind.
- **Common translation errors fixed by hand:**
  - modal verbs (must/should) dropped or mistranslated;
  - domain abbreviations such as CPT and ICD left untranslated or rendered wrongly.
- **Models benchmarked:** Fanar (thinking mode), GPT-4o, DeepSeek-Reasoner and Llama-3.1-8B-Instruct, each in zero-shot and few-shot settings. Few-shot prompting consistently improved results, especially on the conflict class.
- **Hard subset:** OPENCOSS is singled out as difficult because conflict and neutral pairs share heavy lexical overlap.

## Interesting ideas
- Synthetic contradictions are made by deterministic edits (negating, changing a number, flipping a condition) and then deliberately reworded so that surface cues no longer give the label away, which forces a model to reason about meaning rather than spot trigger words
- The translation stage showed that modal verbs are the words that carry obligation in requirements and are the first thing an automatic system silently drops
- Resolution follows a two-way policy: merge both statements into one if that can be done cleanly, otherwise split them into two statements with added conditions, defaults or narrowed scope, while hedges like "if possible" are forbidden
- Few-shot examples are chosen by similarity to the query pair, and the resolution prompt also includes related requirements from the same system as context
- Evaluation highlights a subset where both classes look lexically alike (OPENCOSS), treating it as a stress test for shallow matching

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): the same edit-then-obfuscate recipe could produce contradictory sentence pairs as negative controls, testing whether the world-of-text consistency check catches meaning clashes rather than reacting to "not" or changed digits.
- **I2** (frames): modality (must/should/may) should be an explicit, checked slot on frames, so a lost obligation is visible as a changed frame rather than an unnoticed paraphrase.
- **I3** (back): generation from a conflicting meaning graph could follow the same rule, preferring a single merged clause and otherwise emitting scoped conditional clauses, never hedging vocabulary.
- **I4** (learn): when querying an LLM teacher tier, retrieving nearest already-solved examples plus document-local context should yield more reliable silver labels than fixed generic demonstrations.
- **I5** (answer): QA and test evaluation sets could include a lexically matched hard split, making it obvious whether answers come from structure or from word overlap.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (3): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,085 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,888 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 523 works

🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 1,485 works

## Cite

Cite as: Tymaa Hasanain Hammouda, Alaa Aljabari, Nagham Fahim Hamad and Mustafa Jarrar. 2026. AraREQ: A Dataset and End-to-End System for Conflict Detection and Resolution in Software Requirements. Proceedings of the Fifteenth Language Resources and Evaluation Conference.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{hammouda2026arareq,
  title = {{AraREQ: A Dataset and End-to-End System for Conflict Detection and Resolution in Software Requirements}},
  author = {Tymaa Hasanain Hammouda, Alaa Aljabari, Nagham Fahim Hamad and Mustafa Jarrar},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/357spa5iu5zq},
  url = {https://doi.org/10.63317/357spa5iu5zq}
}
```

- [← previous AraMS-28k: The Largest Publicly Released Line-Level Dataset of Historical Arabic Manuscripts with Margin and Insertion-Anchor Annotations](https://md.research.elamur.ai/free/en/papers/guechaoui-2026-arams-28k-largest-publicly-released/)
- [next → Archaeology at MWE-2026 PARSEME 2.0 Subtasks 1 and 2: Parsing is for Encoders, Paraphrasing is for LLMs](https://md.research.elamur.ai/free/en/papers/roscan-2026-archaeology-mwe-2026-parseme-2/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/4/)

## References

41 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Ummar Abbas, Mohammad Shahmeer Ahmad, Firoj Alam, Enes Altinisik, Ehsannedin Asgari, Yazan Boshmaf, Sabri Boughorbel, Sanjay Chawla, Shammur Chowdhury, et al. 2025. [Fanar: An arabic-centric multimodal generative ai platform](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/). arXiv preprint arXiv:2501.13944. [↗](https://arxiv.org/abs/2501.13944)
2. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv preprint arXiv:2303.08774. [↗](https://arxiv.org/abs/2303.08774)
3. Moustafa Al-Hajj and Mustafa Jarrar. 2021. Arab-GlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 40–48, Online. INCOMA Ltd. [find](https://scholar.google.com/scholar?q=Moustafa+Al%2DHajj+and+Mustafa+Jarrar%2E+2021%2E+Arab%2DGlossBERT%3A+Fine%2DTuning+BERT+on+Context%2DGloss+Pairs+for+WSD%2E+In+Proceedings+of+the+International+Conference+on+Recent+Advances+in+Natural+Language+Proces)
4. Maysoon Aldekhail, Azzedine Chikh, and Djamal Ziani. 2016. Software requirements conflict identification: review and recommendations. Int. J. Adv. Comput. Sci. Appl, 7(10):326–335. [↗](https://doi.org/10.14569/ijacsa.2016.071044)
5. Alaa Aljabari, Lina Duaibes, Mustafa Jarrar, and Mohammed Khalilia. 2024. Event-Arguments Extraction Corpus and Modeling using BERT for Arabic. In Proceedings of the Second Arabic Natural Language Processing Conference (ArabicNLP 2024), Bangkok, Thailand. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Alaa+Aljabari%2C+Lina+Duaibes%2C+Mustafa+Jarrar%2C+and+Mohammed+Khalilia%2E+2024%2E+Event%2DArguments+Extraction+Corpus+and+Modeling+using+BERT+for+Arabic%2E+In+Proceedings+of+the+Second+Arabic+Natural+Language+Pro)
6. Alaa Aljabari, Nagham Hamad, Mohammed Khalilia, and Mustafa Jarrar. 2025a. W ojoodOntology: Ontology-Driven LLM Prompting for Unified Information Extraction Tasks. In Proceedings of the Third Arabic Natural Language Processing Conference (ArabicNLP), Suzhou, China. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Alaa+Aljabari%2C+Nagham+Hamad%2C+Mohammed+Khalilia%2C+and+Mustafa+Jarrar%2E+2025a%2E+W+ojoodOntology%3A+Ontology%2DDriven+LLM+Prompting+for+Unified+Information+Extraction+Tasks%2E+In+Proceedings+of+the+Third+Arabic+N)
7. Alaa Aljabari, Mohammed Khalilia, and Mustafa Jarrar. 2025b. Wojood Relations : Arabic Relation Extraction Corpus and Modeling. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Suzhou, China. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Alaa+Aljabari%2C+Mohammed+Khalilia%2C+and+Mustafa+Jarrar%2E+2025b%2E+Wojood+Relations+%3A+Arabic+Relation+Extraction+Corpus+and+Modeling%2E+In+Proceedings+of+the+2025+Conference+on+Empirical+Methods+in+Natural+La)
8. Fakhraddin Alwajih, Samar M. Magdy, Abdellah El Mekki, Omer Nacar, Youssef Nafea, Safaa Taher Abdelfadil, Abdulfattah Mohammed Yahya, Hamzah Luqman, Nada Almarwani, Samah Aloufi, Baraah Qawasmeh, Houdaifa Atou, Serry Sibaee, Hamzah A. Alsayadi, Walid Al-Dhabyani, Maged S. Al-shaibani, Aya El Aatar, Nour Qandos, Rahaf Alhamouri, Samar Ahmad, Mohammed Anwar Al-Ghrawi, Aminetou Yacoub, Ruwa AbuHweidi, Vatimetou Mohamed Lemin, Reem Abdel-Salam, Ahlam Bashiti, Aisha Alansari, Ahmed Ashraf, Nora Alturayeif, Alcides Alcoba Inciarte, Adel Ammar, Abdelrahim A. Elmadany, Mohamedou Cheikh Tourad, Ismail Berrada, Mustafa Jarrar, Shady Shehata, and Muhammad Abdul-Mageed. 2025a. Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset. In Proceedings of the Third Arabic Natural Language Processing Conference (ArabicNLP), Suzhou, China. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2505.21979)
9. Fakhraddin Alwajih, Abdellah El Mekki, Samar Mohamed Magdy, Abdelrahim A. Elmadany, Omer Nacar, El Moatez Billah Nagoudi, Reem Abdel-Salam, Hanin Atwany, Youssef Nafea, Abdulfattah Mohammed Yahya, Rahaf Alhamouri, Hamzah A. Alsayadi, Hiba Zayed, Sara Shatnawi, Serry Sibaee, Yasir Ech-Chammakhy, Walid Al-Dhabyani, Marwa Mohamed Ali, Imen Jarraya, Ahmed Oumar El-Shangiti, Aisha Alraeesi, Mohammed Anwar Al-Ghrawi, Abdulrahman S. Al-Batati, Elgizouli Mohamed, Noha Taha Elgindi, Muhammed Saeed, Houdaifa Atou, Issam Ait Yahia, Abdelhak Bouayad, Mohammed Machrouh, Amal Makouar, Dania Alkawi, Mukhtar Mohamed, Safaa Taher Abdelfadil, Amine Ziad Ounnoughene, Rouabhia Anfel, Rwaa Assi, Ahmed Sorkatti, Mohamedou Cheikh Tourad, Anis Koubaa, Ismail Berrada, Mustafa Jarrar, Shady Shehata, and Muhammad Abdul-Mageed. 2025b. Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLM. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, pages 0–0, Vienna, Austria. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Fakhraddin+Alwajih%2C+Abdellah+El+Mekki%2C+Samar+Mohamed+Magdy%2C+Abdelrahim+A%2E+Elmadany%2C+Omer+Nacar%2C+El+Moatez+Billah+Nagoudi%2C+Reem+Abdel%2DSalam%2C+Hanin+Atwany%2C+Youssef+Nafea%2C+Abdulfattah+Mohammed+Yahya%2C+Rah)
10. Zaid Alyafeai, Maged Saeed AlShaibani, Badr AlKhamissi, Hamzah Luqman, Ebrahim Alareqi, and Ali Fadel. 2023. [Taqyim: Evaluating arabic NLP tasks using chatgpt models](https://md.research.elamur.ai/free/en/papers/alyafeai-2023-taqyim-evaluating-arabic-nlp-tasks/). CoRR, abs/2306.16322. [↗](https://arxiv.org/abs/2306.16322)
11. Wissam Antoun, Fady Baly, and Hazem M. Hajj. 2020. [Arabert: Transformer-based model for arabic language understanding](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/). CoRR, abs/2003.00104. [↗](https://arxiv.org/abs/2003.00104)
12. Sylvio Barbon Junior, Paolo Ceravolo, Sven Groppe, Mustafa Jarrar, Samira Maghool, Florence Sèdes, Soror Sahri, and Maurice Van Keulen. 2024. Are Large Language Models the New Interface for Data Pipelines? In Proceedings of the International Workshop on Big Data in Emergent Distributed Environments, BiDEDE ’24, New York, NY, USA. Association for Computing Machinery. Ahlam Bashiti, Alaa Aljabari, Hadi Hamoud, Md. [↗](https://doi.org/10.48550/arxiv.2406.06596)
13. Rafiul Biswas, Bilal Shalash, Mustafa Jarrar, Fadi Zaraket, and George Mikros. 2025. ImageEval 2025: The First Arabic Image Captioning Shared Task. In Proceedings of the Third Arabic Natural Language Processing Conference (ArabicNLP), Suzhou, China. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Rafiul+Biswas%2C+Bilal+Shalash%2C+Mustafa+Jarrar%2C+Fadi+Zaraket%2C+and+George+Mikros%2E+2025%2E+ImageEval+2025%3A+The+First+Arabic+Image+Captioning+Shared+Task%2E+In+Proceedings+of+the+Third+Arabic+Natural+Language+)
14. Kareem Darwish, Nizar Habash, Mourad Abbas, Hend Al-Khalifa, Huseein T. Al-Natsheh, Houda Bouamor, Karim Bouzoubaa, Violetta Cavalli-Sforza, Samhaa R. El-Beltagy, Wassim El-Hajj, Mustafa Jarrar, and Hamdy Mubarak. 2021. A Panoramic survey of Natural Language Processing in the Arab Worlds. Commun. ACM, 64(4):72–81. [find](https://scholar.google.com/scholar?q=Kareem+Darwish%2C+Nizar+Habash%2C+Mourad+Abbas%2C+Hend+Al%2DKhalifa%2C+Huseein+T%2E+Al%2DNatsheh%2C+Houda+Bouamor%2C+Karim+Bouzoubaa%2C+Violetta+Cavalli%2DSforza%2C+Samhaa+R%2E+El%2DBeltagy%2C+Wassim+El%2DHajj%2C+Mustafa+Jarrar%2C+and+H)
15. Alessandro Fantechi, Stefania Gnesi, Lucia Passaro, and Laura Semini. 2023. Inconsistency detection in natural language requirements using chatgpt: a preliminary evaluation. In 2023 IEEE 31st International Requirements Engineering Conference (RE), pages 335–340. IEEE. [find](https://scholar.google.com/scholar?q=Alessandro+Fantechi%2C+Stefania+Gnesi%2C+Lucia+Passaro%2C+and+Laura+Semini%2E+2023%2E+Inconsistency+detection+in+natural+language+requirements+using+chatgpt%3A+a+preliminary+evaluation%2E+In+2023+IEEE+31st+Internat)
16. Alessio Ferrari, Giorgio Oronzo Spagnolo, and Stefania Gnesi. 2017. Pure: A dataset of public requirements documents. In 2017 IEEE 25th international requirements engineering conference (RE), pages 502–505. IEEE. [find](https://scholar.google.com/scholar?q=Alessio+Ferrari%2C+Giorgio+Oronzo+Spagnolo%2C+and+Stefania+Gnesi%2E+2017%2E+Pure%3A+A+dataset+of+public+requirements+documents%2E+In+2017+IEEE+25th+international+requirements+engineering+conference+%28RE%29%2C+pages+50)
17. Alexander Elenga Gärtner and Dietmar Göhlich. 2024a. Automated requirement contradiction detection through formal logic and llms. Automated Software Engineering, 31(2):49. [↗](https://doi.org/10.1007/s10515-024-00452-x)
18. Alexander Elenga Gärtner and Dietmar Göhlich. 2024b. Towards an automatic contradiction detection in requirements engineering. Proceedings of the Design Society, 4:2049–2058. [↗](https://doi.org/10.1017/pds.2024.207)
19. Weize Guo, Li Zhang, and Xiaoli Lian. 2021. Automatically detecting the conflicts between software requirements based on finer semantic analysis. arXiv preprint arXiv:2103.02255. [↗](https://arxiv.org/abs/2103.02255)
20. Karim El Haff, Mustafa Jarrar, Tymaa Hammouda, and Fadi Zaraket. 2022. Curras + Baladi: Towards a Levantine Corpus. In Proceedings of the International Conference on Language Resources and Evaluation(LREC 2022), Marseille, France. [↗](https://doi.org/10.48550/arxiv.2205.09692)
21. Nagham Hamad, Mohammed Khalilia, and Mustafa Jarrar. 2025. Konooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, page 7316–7331, Vienna, Austria. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Nagham+Hamad%2C+Mohammed+Khalilia%2C+and+Mustafa+Jarrar%2E+2025%2E+Konooz%3A+Multi%2Ddomain+Multi%2Ddialect+Corpus+for+Named+Entity+Recognition%2E+In+Proceedings+of+the+63rd+Annual+Meeting+of+the+Association+for+Comp)
22. Robert K Helmeczi, Mucahit Cevik, and Savas Yıldırım. 2022. A prompt-based few-shot learning approach to software conflict detection. arXiv preprint arXiv:2211.02709. [↗](https://arxiv.org/abs/2211.02709)
23. Florence Ho, Ruben Geraldes, Artur Goncalves, Bastien Rigault, Atsushi Oosedo, Marc Cavazza, and Helmut Prendinger. 2019. Pre-flight conflict detection and resolution for uav integration in shared airspace: Sendai 2030 model case. IEEE Access, 7:170226–170237. [↗](https://doi.org/10.1109/access.2019.2954987)
24. Mustafa Jarrar. 2021. [The Arabic Ontology - An Arabic Wordnet with Ontologically Clean Content](https://md.research.elamur.ai/free/en/papers/jarrar-2022-arabic-ontology-arabic-wordnet-ontologically/). Applied Ontology Journal, 16(1):1–26. [↗](https://doi.org/10.3233/ao-200241)
25. Mustafa Jarrar, Muhammad Abdul-Mageed, Mohammed Khalilia, Bashar Talafha, AbdelRahim Elmadany, Nagham Hamad, and Alaa’ Omar. 2023a. WojoodNER 2023: The First Arabic Named Entity Recognition Shared Task. In Proceedings of the 1st Arabic Natural Language Processing Conference (ArabicNLP), Part of the EMNLP 2023, pages 748–758. ACL. [find](https://scholar.google.com/scholar?q=Mustafa+Jarrar%2C+Muhammad+Abdul%2DMageed%2C+Mohammed+Khalilia%2C+Bashar+Talafha%2C+AbdelRahim+Elmadany%2C+Nagham+Hamad%2C+and+Alaa%E2%80%99+Omar%2E+2023a%2E+WojoodNER+2023%3A+The+First+Arabic+Named+Entity+Recognition+Shared+Tas)
26. Mustafa Jarrar, Anton Deik, and Bilal Faraj. 2011. Ontology-based data and process governance framework -the case of e-government interoperability in palestine. In Proceedings of the IFIP International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA’11), pages 83– 98. [find](https://scholar.google.com/scholar?q=Mustafa+Jarrar%2C+Anton+Deik%2C+and+Bilal+Faraj%2E+2011%2E+Ontology%2Dbased+data+and+process+governance+framework+%2Dthe+case+of+e%2Dgovernment+interoperability+in+palestine%2E+In+Proceedings+of+the+IFIP+Internationa)
27. Mustafa Jarrar and Tymaa Hasanain Hammouda. 2024. Qabas: An Open-Source Arabic Lexicographic Database. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13363–13370, Torino, Italy. ELRA and ICCL. [find](https://scholar.google.com/scholar?q=Mustafa+Jarrar+and+Tymaa+Hasanain+Hammouda%2E+2024%2E+Qabas%3A+An+Open%2DSource+Arabic+Lexicographic+Database%2E+In+Proceedings+of+the+2024+Joint+International+Conference+on+Computational+Linguistics%2C+Language+)
28. Mustafa Jarrar, Sanad Malaysha, Tymaa Hammouda, and Mohammed Khalilia. 2023b. SALMA: Arabic Sense-annotated Corpus and WSD Benchmarks. In Proceedings of the 1st Arabic Natural Language Processing Conference (ArabicNLP), Part of the EMNLP 2023, pages 359–369. ACL. [find](https://scholar.google.com/scholar?q=Mustafa+Jarrar%2C+Sanad+Malaysha%2C+Tymaa+Hammouda%2C+and+Mohammed+Khalilia%2E+2023b%2E+SALMA%3A+Arabic+Sense%2Dannotated+Corpus+and+WSD+Benchmarks%2E+In+Proceedings+of+the+1st+Arabic+Natural+Language+Processing+Conf)
29. Mustafa Jarrar, Fadi Zaraket, Tymaa Hammouda, Daanish Masood Alavi, and Martin Waahlisch. 2023c. Lisan: Yemeni, Irqi, Libyan, and Sudanese Arabic Dialect Copora with Morphological Annotations. In The 20th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), pages 1–7. IEEE. [find](https://scholar.google.com/scholar?q=Mustafa+Jarrar%2C+Fadi+Zaraket%2C+Tymaa+Hammouda%2C+Daanish+Masood+Alavi%2C+and+Martin+Waahlisch%2E+2023c%2E+Lisan%3A+Yemeni%2C+Irqi%2C+Libyan%2C+and+Sudanese+Arabic+Dialect+Copora+with+Morphological+Annotations%2E+In+The+)
30. Mohammed Khalilia, Sanad Malaysha, Reem Suwaileh, Mustafa Jarrar, Alaa Aljabari, Tamer Elsayed, and Imed Zitouni. 2024. ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task. In Proceedings of the Second Arabic Natural Language Processing Conference (ArabicNLP 2024), Bangkok, Thailand. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Mohammed+Khalilia%2C+Sanad+Malaysha%2C+Reem+Suwaileh%2C+Mustafa+Jarrar%2C+Alaa+Aljabari%2C+Tamer+Elsayed%2C+and+Imed+Zitouni%2E+2024%2E+ArabicNLU+2024%3A+The+First+Arabic+Natural+Language+Understanding+Shared+Task%2E+In+)
31. Xabier Larrucea, Annie Combelles, and John Favaro. 2013. Safety-critical software [guest editors’ introduction]. IEEE Software, 30(3):25–27. [↗](https://doi.org/10.1109/ms.2013.55)
32. Haneen Liqreina, Mustafa Jarrar, Mohammed Khalilia, Ahmed Oumar El-Shangiti, and Muhammad Abdul-Mageed. 2023. Arabic Fine-Grained Entity Recognition. In Proceedings of the 1st Arabic Natural Language Processing Conference (ArabicNLP), Part of the EMNLP 2023, pages 310–323. ACL. [find](https://scholar.google.com/scholar?q=Haneen+Liqreina%2C+Mustafa+Jarrar%2C+Mohammed+Khalilia%2C+Ahmed+Oumar+El%2DShangiti%2C+and+Muhammad+Abdul%2DMageed%2E+2023%2E+Arabic+Fine%2DGrained+Entity+Recognition%2E+In+Proceedings+of+the+1st+Arabic+Natural+Language+)
33. Lovish Madaan, David Esiobu, Pontus Stenetorp, Barbara Plank, and Dieuwke Hupkes. 2025. Lost in inference: Rediscovering the role of natural language inference for large language models. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 9229–9242, Albuquerque, New Mexico. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2411.14103)
34. Garima Malik, Mucahit Cevik, and Ayşe Başar. 2023a. Data augmentation for conflict and duplicate detection in software engineering sentence pairs. arXiv preprint arXiv:2305.09608. [↗](https://arxiv.org/abs/2305.09608)
35. Garima Malik, Mucahit Cevik, Devang Parikh, and Ayse Basar. 2022. Supervised semantic similarity-based conflict detection algorithm: S3cda. arXiv preprint arXiv:2206.13690. [↗](https://arxiv.org/abs/2206.13690)
36. Garima Malik, Savas Yildirim, Mucahit Cevik, Ayse Bener, and Devang Parikh. 2023b. Transfer learning for conflict and duplicate detection in software requirement pairs. arXiv preprint arXiv:2301.03709. Amal Nayouf, Mustafa Jarrar, Fadi zaraket, Tymaa Hammouda, and Mohamad-Bassam Kurdy. 2023. [Nâbra: Syrian Arabic Dialects with Morphological Annotations](https://md.research.elamur.ai/free/en/papers/nayouf-2023-nabra-syrian-arabic-dialects-morphological/). In Proceedings of the 1st Arabic Natural Language Processing Conference (ArabicNLP), Part of the EMNLP 2023, pages 12–23. ACL. [↗](https://arxiv.org/abs/2301.03709)
37. Mohd Hafeez Osman and Mohd Firdaus Zaharin. 2018. Ambiguous software requirement specification detection: An automated approach. In Proceedings of the 5th International Workshop on Requirements Engineering and Testing, pages 33–40. [find](https://scholar.google.com/scholar?q=Mohd+Hafeez+Osman+and+Mohd+Firdaus+Zaharin%2E+2018%2E+Ambiguous+software+requirement+specification+detection%3A+An+automated+approach%2E+In+Proceedings+of+the+5th+International+Workshop+on+Requirements+Engine)
38. Summra Saleem, Muhammad Nabeel Asim, and Andreas Dengel. 2025. Passionnet: An innovative framework for duplicate and conflicting requirements identification. Expert Systems with Applications, page 128684. [↗](https://doi.org/10.1016/j.eswa.2025.128684)
39. Bashar Talafha, Karima Kadaoui, Samar Mohamed Magdy, Mariem Habiboullah, Chafei Mohamed Chafei, Ahmed Oumar El-Shangiti, Hiba Zayed, Mohamedou Cheikh Tourad, Rahaf Alhamouri, Rwaa Assi, Aisha Alraeesi, Hour Mohamed, Fakhraddin Alwajih, Abdelrahman Mohamed, Abdellah El Mekki, El Moatez Billah Nagoudi, Benelhadj Djelloul Mama Saadia, Hamzah A. Alsayadi, Walid Al-Dhabyani, Sara Shatnawi, Yasir Ech-chammakhy, Amal Makouar, Yousra Berrachedi, Mustafa Jarrar, Shady Shehata, Ismail Berrada, and Muhammad Abdul-Mageed. 2024. Casablanca: Data and Models for Multidialectal Arabic Speech Recognition. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, USA. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2410.04527)
40. Simge Yatkın and Tolga Ovatman. 2024. Logical analysis and contradiction detection in high-level requirements during the review process using sat-solver. arXiv preprint arXiv:2405.00163. [↗](https://arxiv.org/abs/2405.00163)
41. Umi Laili Yuhana, Siti Rochimah, et al. 2024. Conflict detection of functional requirements based on clustering and rule-based system. IEEE Access. [find](https://scholar.google.com/scholar?q=Umi+Laili+Yuhana%2C+Siti+Rochimah%2C+et+al%2E+2024%2E+Conflict+detection+of+functional+requirements+based+on+clustering+and+rule%2Dbased+system%2E+IEEE+Access%2E)

*Updated 24 October 2026*