--- title: "Athership at AraGenEval Shared Task: Identifying Arabic" description: "This work addresses closed-set authorship attribution for Arabic: given one paragraph, the system must… 6 references, cited by 1 paper here." authors: ["Mohamed Amin", "Mahmoud Rady", "Mariam Hossam", "Sara Gaballa", "Eman Samir", "Maria Bassem", "Nisreen Hisham", "Ayman Khalafallah"] year: 2025 venue: "Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks" source: "https://aclanthology.org/2025.arabicnlp-sharedtasks.9/" doi: "10.18653/v1/2025.arabicnlp-sharedtasks.9" 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/amin-2025-athership-arageneval-shared-task-identifying/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/amin-2025-athership-arageneval-shared-task-identifying/) [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/) / Athership at AraGenEval Shared Task: Identifying Arabic Authorship with a Dual-Model Logit Fusion # Athership at AraGenEval Shared Task: Identifying Arabic Authorship with a Dual-Model Logit Fusion **Authors:** [Mohamed Amin](https://md.research.elamur.ai/free/en/authors/mohamed-amin/), Mahmoud Rady, Mariam Hossam, Sara Gaballa, Eman Samir, Maria Bassem, Nisreen Hisham and [Ayman Khalafallah](https://md.research.elamur.ai/free/en/authors/ayman-khalafallah/) · **Year:** 2025 · **Venue:** [Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks](https://md.research.elamur.ai/free/en/venues/arabicnlp/) ([ArabicNLP 2025](https://md.research.elamur.ai/free/en/venues/arabicnlp/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.arabicnlp-sharedtasks.9/) · [PDF](https://aclanthology.org/2025.arabicnlp-sharedtasks.9.pdf) · [DOI 10.18653/v1/2025.arabicnlp-sharedtasks.9](https://doi.org/10.18653/v1/2025.arabicnlp-sharedtasks.9) · [OpenAlex](https://openalex.org/W4416037780) **Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/amin-2025-athership-arageneval-shared-task-identifying/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary This work addresses closed-set authorship attribution for Arabic: given one paragraph, the system must name its writer from 21 candidates. It was entered in Subtask 2 of AraGenEval 2025, a shared task held as part of ArabicNLP. The task is difficult in Arabic because of root-based morphology, diglossia and noisy spelling, such as hamza variants and optional diacritics. The organizers' data, cut from literary, philosophical and journalistic books, adds further problems: class sizes are imbalanced and paragraph lengths vary widely. The team fine-tuned two Arabic encoders that were pretrained with different objectives: - AraBERT, trained with MLM. - AraELECTRA, trained with RTD. Each model got a linear head on [CLS], was trained for 4 epochs, and saw inputs truncated to 512 tokens. Preprocessing was kept minimal so that stylistic cues would survive. The two models' raw logits are combined before a single softmax. This fused system beat both individual models on the dev set and placed third on the test leaderboard. Comparisons against a sliding-window model, a TF-IDF model and a contrastive model suggest that pretrained encoders and unbroken context matter for capturing style. ## Key points - **Test result:** third place in Subtask 2, with Macro-F1 0.85968 and accuracy 0.89516. - **Dev scores (accuracy / Macro-F1):** - AraBERT: 0.90 / 0.84 - AraELECTRA: 0.88 / 0.83 - Logit ensemble: 0.92 / 0.86 - **Other dev results:** - TF-IDF over character and word n-grams, fed to a 2-layer FCN: about 0.75 / 0.70. - Contrastive learning on Qarib embeddings with k-NN: 0.84 / 0.79. - **Sliding-window variant:** aubmindlab/bert-large-AraBERTv02 with 512-token windows, stride 128, balanced class weights, label smoothing 0.1 and chunk voting at inference. It reached 0.90 / 0.85, which is better than either single model but below the ensemble. Separately, naively splitting long texts and copying the document label to every chunk lowered accuracy. - **Fusion details are inconsistent in the paper.** The fusion uses equal, untuned weights. The formula writes plain summation with 11-dimensional logits, while the prose calls it averaging over 21 authors. Learned weights and more ensemble members are listed as future work. ## Interesting ideas - Fusing raw pre-softmax scores from models trained with different objectives keeps each model's decision margins, which is richer than voting on final labels. - Copying a document-level label onto every fragment of a split text produced noisy training examples, because short pieces often lack the evidence the label depends on. - Light preprocessing was chosen on purpose, because aggressive normalization can erase exactly the surface signal (spelling variants, punctuation) the task relies on. - A shallow TF-IDF n-gram model served as a floor that showed how much the contextual encoders really add. - Reporting Macro-F1 next to accuracy exposed how well the system handles minority classes in an imbalanced set. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (tree): sum or interpolate arc and label scores from the perceptron parser and gparse before decoding, instead of reconciling finished trees. - **I2** (learn): when silver sentences or passages are segmented, re-check each fragment's label with the witness parsers rather than inheriting it blindly. - **I3** (tokens): keep the raw surface form next to the normalized form and lemma, so later stages can still use orthographic cues when needed. - **I4** (learn): use a bag-of-words classifier as a standing negative control in evaluations, so gains from the explainable pipeline are measured against what shallow lexical overlap already achieves. - **I5** (answer): score test sets per question type with macro averaging, so rare kinds of questions are not hidden behind frequent easy ones. *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 (5): [🖥️ 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 · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works 🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works ## Cite Cite as: Mohamed Amin, Mahmoud Rady, Mariam Hossam, Sara Gaballa, Eman Samir, Maria Bassem, Nisreen Hisham and Ayman Khalafallah. 2025. Athership at AraGenEval Shared Task: Identifying Arabic Authorship with a Dual-Model Logit Fusion. Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{amin2025athership, title = {{Athership at AraGenEval Shared Task: Identifying Arabic Authorship with a Dual-Model Logit Fusion}}, author = {Mohamed Amin, Mahmoud Rady, Mariam Hossam, Sara Gaballa, Eman Samir, Maria Bassem, Nisreen Hisham and Ayman Khalafallah}, booktitle = {Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks}, year = {2025}, doi = {10.18653/v1/2025.arabicnlp-sharedtasks.9}, url = {https://doi.org/10.18653/v1/2025.arabicnlp-sharedtasks.9} } ``` - [← previous ASR Models for Traditional Emirati Arabic: Challenges, Adaptations, and Performance Evaluation](https://md.research.elamur.ai/free/en/papers/alblooki-2025-asr-models-traditional-emirati-arabic/) - [next → 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/) - [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/14/) ## References 6 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish, and Younes Samih. 2021. [Pre-training bert on arabic tweets: Practical considerations](https://md.research.elamur.ai/free/en/papers/abdelali-2021-pre-training-bert-arabic-tweets/). [↗](https://doi.org/10.48550/arxiv.2102.10684) 2. Zahra Jafari Akinabad and Masoud Mohammadpour. 2021. Ensemble learning methods in authorship attribution. In 2021 7th International Conference on Web Research (ICWR), pages 1–6. IEEE. [find](https://scholar.google.com/scholar?q=Zahra+Jafari+Akinabad+and+Masoud+Mohammadpour%2E+2021%2E+Ensemble+learning+methods+in+authorship+attribution%2E+In+2021+7th+International+Conference+on+Web+Research+%28ICWR%29%2C+pages+1%E2%80%936%2E+IEEE%2E) 3. Muhammad Abdul-Mageed, AbdelRahim Elmadany, and El Moatez Billah Nagoudi. 2021. [ARBERT & MARBERT: Deep bidirectional transformers for Arabic](https://md.research.elamur.ai/free/en/papers/abdul-mageed-2021-arbert-marbert-deep-bidirectional-transformers/). In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7088–7105, Online. Association for Computational Linguistics. [↗](https://doi.org/10.18653/v1/2021.acl-long.551) 4. Zaid Shaker. 2017. Arabic authorship identification using n-gram and support vector machine. In International Conference on Advanced Machine Learning Technologies and Applications, pages 507–516. Springer. [find](https://scholar.google.com/scholar?q=Zaid+Shaker%2E+2017%2E+Arabic+authorship+identification+using+n%2Dgram+and+support+vector+machine%2E+In+International+Conference+on+Advanced+Machine+Learning+Technologies+and+Applications%2C+pages+507%E2%80%93516%2E+Spri) 5. Efstathios Stamatatos. 2009. A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology, 60(3):538–556. [↗](https://doi.org/10.1002/asi.21001) 6. Shadi Abudalfa, Saad Ezzini, Ahmed Abdelali, Hamza Alami, Abdessamad Benlahbib, Salmane Chafik, Mo El-Haj, Abdelkader El Mahdaouy, Mustafa Jarrar, Salima Lamsiyah, and Hamzah Luqman. 2025. [The AraGenEval Shared Task on Arabic Authorship Style Transfer and AI-generated Text Detection](https://md.research.elamur.ai/free/en/papers/abudalfa-2025-arageneval-shared-task-arabic-authorship/). In Proceedings of the Third Arabic Natural Language Processing Conference (ArabicNLP 2025). Association for Computational Linguistics. *Updated 1 November 2026*