--- title: "Automated Generation of Arabic Verb Conjugations with" description: "The authors present the work as the first bilingual Arabic–Urdu conjugation resource, mainly for… 3 references, cited by 1 paper here." authors: ["Haq Nawaz", "Manal Elobaid", "Ali Al-Laith", "Saif Ullah"] year: 2025 venue: "Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script" source: "https://aclanthology.org/2025.abjadnlp-1.14/" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/nawaz-2025-automated-generation-arabic-verb-conjugations/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/nawaz-2025-automated-generation-arabic-verb-conjugations/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2025](https://md.research.elamur.ai/free/en/papers/2025/page/14/) / Automated Generation of Arabic Verb Conjugations with Multilingual Urdu Translation: An NLP Approach # Automated Generation of Arabic Verb Conjugations with Multilingual Urdu Translation: An NLP Approach **Authors:** [Haq Nawaz](https://md.research.elamur.ai/free/en/authors/haq-nawaz/), Manal Elobaid, [Ali Al-Laith](https://md.research.elamur.ai/free/en/authors/ali-al-laith/) and Saif Ullah · **Year:** 2025 · **Venue:** [Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script](https://md.research.elamur.ai/free/en/venues/abjadnlp/) ([Workshop on NLP for Languages Using Arabic Script 2025](https://md.research.elamur.ai/free/en/venues/abjadnlp/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2025.abjadnlp-1.14/) · [PDF](https://aclanthology.org/2025.abjadnlp-1.14.pdf) · [OpenAlex](https://openalex.org/W7153678545) **Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read ## Summary Arabic verbs inflect for tense, person, gender and number, and many forms are marked only by diacritics. This makes full conjugation tables hard to produce automatically. Urdu learners also have few computational resources that link these forms to their own language. The authors built a rule-based generator for triliteral, non-weak Classical Arabic roots. Its coverage spans four tense types: the Past Simple and the Present Simple, each in both affirmative and negative form. The roots are frequent in the Quran and classical literature and were checked against the Quranic Arabic Corpus. Each root is matched to regex-style lexeme templates (Baab patterns). Rules then attach prefixes and suffixes and apply phonetic adjustments. A parallel module fills predefined Urdu templates that agree with the Arabic form's features. The released dataset was reviewed by scholars of Jamia Ashrafia, Lahore. A comparison with Google Translate exposed grammatical errors by the general-purpose system. The authors present the work as the first bilingual Arabic–Urdu conjugation resource, mainly for education and religious study. ## Key points - The system covers only triliteral, non-weak roots. Weak and quadrilateral roots, dialects, Persian and Pashto are left for future work. - The authors report 200 roots with 78 variants each, giving 14,400 entries with Urdu translations. Note that the stated numbers do not multiply out (200 × 78 = 15,600). - Roots were taken from Quranic and Classical Arabic sources and validated with the Quranic Arabic Corpus. - Validation combined two checks: - comparison with reference conjugation patterns; - manual review by Jamia Ashrafia scholars. - The dataset is public on GitHub (haqnawaz99/Arabic-Urdu-Conjugation-Dataset). Future plans mention AraBERT or multilingual BERT to generalize to unseen roots. ## Interesting ideas - Full inflection tables are produced exhaustively from a root plus a pattern class, so coverage is systematic rather than corpus-dependent. - The target-language phrase is chosen by the same feature bundle (person, gender, number, tense, polarity) that produced the source form, not by string mapping. - Something one language encodes in a suffix (Arabic dual) must be spelled out with extra words and reordering in the other (Urdu "those two men"). Number therefore has to be kept as meaning, not as surface form. - Negation is treated as a full dimension of the paradigm, yielding a crossed grid of feature combinations that can serve as a test suite. - Expert review of generated output is paired with a general-purpose MT baseline to show where generic systems lose grammatical detail. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (tokens): generate complete English paradigms from lemma and inflection class, then diff them against the morphology lexicon to find gaps and irregular-form errors. - **I2** (back): realize text from frame features via per-feature templates, so each output word traces back to an explicit grammatical decision. - **I3** (world): store grammatical number, including "exactly two", as an entity property, so translation through the meta-world can choose morphology or periphrasis per language. - **I4** (learn): build controlled grids of polarity × tense × person sentences as negative-control evaluations, checking that frames flip polarity and nothing else. - **I5** (learn): compare Mova's explainable outputs with an LLM baseline on minimal feature contrasts, so specific failure categories become visible. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [Qatar University](https://md.research.elamur.ai/free/en/universities/qatar-university/) — University, Doha, Qatar · 48 works · 1,026 citations - [COMSATS University Islamabad](https://md.research.elamur.ai/free/en/universities/comsats-university-islamabad/) — University, Islamabad, Pakistan · 2 works · 6 citations 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works 🧭 Topics (2): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works · [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · 828 works ## Cite Cite as: Haq Nawaz, Manal Elobaid, Ali Al-Laith and Saif Ullah. 2025. Automated Generation of Arabic Verb Conjugations with Multilingual Urdu Translation: An NLP Approach. Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{nawaz2025automated, title = {{Automated Generation of Arabic Verb Conjugations with Multilingual Urdu Translation: An NLP Approach}}, author = {Haq Nawaz, Manal Elobaid, Ali Al-Laith and Saif Ullah}, booktitle = {Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script}, year = {2025}, url = {https://aclanthology.org/2025.abjadnlp-1.14/} } ``` - [← previous AutoArabic: A Three-Stage Framework for Localizing Video-Text Retrieval Benchmarks](https://md.research.elamur.ai/free/en/papers/eltahir-2025-autoarabic-three-stage-framework-localizing/) - [next → Automatic Detection of Inauthentic Templated Responses in English Language Assessments](https://md.research.elamur.ai/free/en/papers/samant-2025-automatic-detection-inauthentic-templated-responses/) - [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/14/) ## References 3 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. J. Alasmari, J.C.E. Watson, and E. Atwell. Investigating the Rate of Agree- ment and Disagreement of Tense and Aspect of Quranic Verbs in Arabic to English Translations: Experimental Results and Analysis,” International Journal on Islamic Applications in Computer Science and Technology, vol. 6, no. 1, pp. 1-10, 2018. [find](https://scholar.google.com/scholar?q=J%2E+Alasmari%2C+J%2EC%2EE%2E+Watson%2C+and+E%2E+Atwell%2E+Investigating+the+Rate+of+Agree%2D+ment+and+Disagreement+of+Tense+and+Aspect+of+Quranic+Verbs+in+Arabic+to+English+Translations%3A+Experimental+Results+and+Analy) 2. M.T. Ben Othman, M.A. Al-Hagery, and Y.M. El Hashemi, Arabic Text Processing Model: Verbs Roots and Conjugation Automation, IEEE Access, vol. 8, pp. 103913–103923, 2020. [find](https://scholar.google.com/scholar?q=M%2ET%2E+Ben+Othman%2C+M%2EA%2E+Al%2DHagery%2C+and+Y%2EM%2E+El+Hashemi%2C+Arabic+Text+Processing+Model%3A+Verbs+Roots+and+Conjugation+Automation%2C+IEEE+Access%2C+vol%2E+8%2C+pp%2E+103913%E2%80%93103923%2C+2020%2E) 3. A.N. Alsaleh, E. Atwell, and A. Altahhan, Quranic Verses Semantic Relat- edness Using AraBERT, University of Leeds, 2021. [find](https://scholar.google.com/scholar?q=A%2EN%2E+Alsaleh%2C+E%2E+Atwell%2C+and+A%2E+Altahhan%2C+Quranic+Verses+Semantic+Relat%2D+edness+Using+AraBERT%2C+University+of+Leeds%2C+2021%2E) *Updated 31 October 2026*