@inproceedings{melhem-etal-2026-yafare,
title = "{Y}afa{RE} at {K}nowledge{G}raph{E}val 2026: A Relation Extraction {A}r{BERT}v2-based Approach for {A}rabic Knowledge Graph Construction",
author = "Melhem, Anas and
Hamed, Osama and
Shawareb, Noora",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference: Shared Tasks",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-sharedtasks.155/",
pages = "1238--1243",
ISBN = "979-8-89176-497-2",
abstract = "Relation extraction (RE) identifies semantic relations between entities, converting unstructured text into structured facts for downstream applications such as knowledge graph construction. Arabic RE remains challenging due to rich morphology, complex syntax, dialectal variation, and the scarcity of high-quality annotated resources. We present Yafa{'}s Team submission (ranked 7th) to Subtask 2 of the KnowledgeGraphEval 2026 Shared Task, which frames Arabic RE as multiclass classification over 40 relation types from the dataset. We propose an ArBERTv2-based entity-marker classifier that fuses global sentence context ($[CLS]$) with entity-specific representations (h{\_}e1, h{\_}e2) into a joint representation for relation prediction. Building on this baseline, we exploit a transductive self-training step that pseudo-labels the released test inputs at a softmax confidence threshold of 0.95 and retrains on them, raising Micro-F1 from 70.20{\%} to 91.92{\%}. We further conduct a systematic analysis of cross-backbone ensembling with MARBERTv2 and AraBERTv2, finding that ensembling does not outperform the single transductive model. Our results highlight transductive self-training as a simple yet effective strategy for improving Arabic RE robustness."
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<abstract>Relation extraction (RE) identifies semantic relations between entities, converting unstructured text into structured facts for downstream applications such as knowledge graph construction. Arabic RE remains challenging due to rich morphology, complex syntax, dialectal variation, and the scarcity of high-quality annotated resources. We present Yafa’s Team submission (ranked 7th) to Subtask 2 of the KnowledgeGraphEval 2026 Shared Task, which frames Arabic RE as multiclass classification over 40 relation types from the dataset. We propose an ArBERTv2-based entity-marker classifier that fuses global sentence context ([CLS]) with entity-specific representations (h_e1, h_e2) into a joint representation for relation prediction. Building on this baseline, we exploit a transductive self-training step that pseudo-labels the released test inputs at a softmax confidence threshold of 0.95 and retrains on them, raising Micro-F1 from 70.20% to 91.92%. We further conduct a systematic analysis of cross-backbone ensembling with MARBERTv2 and AraBERTv2, finding that ensembling does not outperform the single transductive model. Our results highlight transductive self-training as a simple yet effective strategy for improving Arabic RE robustness.</abstract>
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%0 Conference Proceedings
%T YafaRE at KnowledgeGraphEval 2026: A Relation Extraction ArBERTv2-based Approach for Arabic Knowledge Graph Construction
%A Melhem, Anas
%A Hamed, Osama
%A Shawareb, Noora
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-497-2
%F melhem-etal-2026-yafare
%X Relation extraction (RE) identifies semantic relations between entities, converting unstructured text into structured facts for downstream applications such as knowledge graph construction. Arabic RE remains challenging due to rich morphology, complex syntax, dialectal variation, and the scarcity of high-quality annotated resources. We present Yafa’s Team submission (ranked 7th) to Subtask 2 of the KnowledgeGraphEval 2026 Shared Task, which frames Arabic RE as multiclass classification over 40 relation types from the dataset. We propose an ArBERTv2-based entity-marker classifier that fuses global sentence context ([CLS]) with entity-specific representations (h_e1, h_e2) into a joint representation for relation prediction. Building on this baseline, we exploit a transductive self-training step that pseudo-labels the released test inputs at a softmax confidence threshold of 0.95 and retrains on them, raising Micro-F1 from 70.20% to 91.92%. We further conduct a systematic analysis of cross-backbone ensembling with MARBERTv2 and AraBERTv2, finding that ensembling does not outperform the single transductive model. Our results highlight transductive self-training as a simple yet effective strategy for improving Arabic RE robustness.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.155/
%P 1238-1243
Markdown (Informal)
[YafaRE at KnowledgeGraphEval 2026: A Relation Extraction ArBERTv2-based Approach for Arabic Knowledge Graph Construction](https://aclanthology.org/2026.arabicnlp-sharedtasks.155/) (Melhem et al., ArabicNLP 2026)
ACL