@inproceedings{ho-van-2026-adaptlinker,
title = "{A}dapt{L}inker at {K}nowledge{G}raph{E}val 2026: Cross-Domain {A}rabic Named Entity Recognition with Adversarial and Self-Training",
author = "Ho, Quan Hoang and
V{\u{a}}n, Th{\`i}n {\DJ}ặng",
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.149/",
pages = "1195--1199",
ISBN = "979-8-89176-497-2",
abstract = "This paper presents the submission of team AdaptLinker to the AdaptNER shared task (Subtask 1: Cross-Domain Named Entity Recognition) at KnowledgeGraphEval 2026. To address nested entities and domain shift across 10 unseen target domains, we propose a multi-head token classification framework built on the AraBERT backbone. Our approach inte- grates: (1) 21 parallel linear classifiers to pre- dict overlapping named entities; (2) a Domain Adversarial Neural Network (DANN) using a Gradient Reversal Layer (GRL) to align feature spaces; (3) confidence-gated pseudo-labeling on unlabeled target text; and (4) Stochastic Weight Averaging (SWA). Our final system achieved a Precision of 75.48{\%}, Recall of 71.21{\%}, a Micro F1 of 73.29{\%}, and a Macro F1 of 63.37{\%} on the official blinded test set, outperforming the shared-task baseline (0.7329 vs. 0.622 Micro F1) and ranking 6th out of all participating teams"
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<abstract>This paper presents the submission of team AdaptLinker to the AdaptNER shared task (Subtask 1: Cross-Domain Named Entity Recognition) at KnowledgeGraphEval 2026. To address nested entities and domain shift across 10 unseen target domains, we propose a multi-head token classification framework built on the AraBERT backbone. Our approach inte- grates: (1) 21 parallel linear classifiers to pre- dict overlapping named entities; (2) a Domain Adversarial Neural Network (DANN) using a Gradient Reversal Layer (GRL) to align feature spaces; (3) confidence-gated pseudo-labeling on unlabeled target text; and (4) Stochastic Weight Averaging (SWA). Our final system achieved a Precision of 75.48%, Recall of 71.21%, a Micro F1 of 73.29%, and a Macro F1 of 63.37% on the official blinded test set, outperforming the shared-task baseline (0.7329 vs. 0.622 Micro F1) and ranking 6th out of all participating teams</abstract>
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%0 Conference Proceedings %T AdaptLinker at KnowledgeGraphEval 2026: Cross-Domain Arabic Named Entity Recognition with Adversarial and Self-Training %A Ho, Quan Hoang %A Văn, Thìn Đặng %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 ho-van-2026-adaptlinker %X This paper presents the submission of team AdaptLinker to the AdaptNER shared task (Subtask 1: Cross-Domain Named Entity Recognition) at KnowledgeGraphEval 2026. To address nested entities and domain shift across 10 unseen target domains, we propose a multi-head token classification framework built on the AraBERT backbone. Our approach inte- grates: (1) 21 parallel linear classifiers to pre- dict overlapping named entities; (2) a Domain Adversarial Neural Network (DANN) using a Gradient Reversal Layer (GRL) to align feature spaces; (3) confidence-gated pseudo-labeling on unlabeled target text; and (4) Stochastic Weight Averaging (SWA). Our final system achieved a Precision of 75.48%, Recall of 71.21%, a Micro F1 of 73.29%, and a Macro F1 of 63.37% on the official blinded test set, outperforming the shared-task baseline (0.7329 vs. 0.622 Micro F1) and ranking 6th out of all participating teams %U https://aclanthology.org/2026.arabicnlp-sharedtasks.149/ %P 1195-1199
Markdown (Informal)
[AdaptLinker at KnowledgeGraphEval 2026: Cross-Domain Arabic Named Entity Recognition with Adversarial and Self-Training](https://aclanthology.org/2026.arabicnlp-sharedtasks.149/) (Ho & Văn, ArabicNLP 2026)