@inproceedings{shoumi-etal-2026-armyforre,
title = "{A}rmy{F}or{RE} at {K}nowledge{G}raph{E}val 2026: Typed Entity Markers and Imbalance-Aware Training for {A}rabic Relation Extraction",
author = "Shoumi, Milyun Ni{'}ma and
Syulistyo, Arie Rachmad and
Azizah, Aulia Resty and
Putra, Savero Athallah Hardiana",
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.158/",
pages = "1255--1260",
ISBN = "979-8-89176-497-2",
abstract = "In this paper, we present a relation extraction system developed for Subtask 2 of KnowledgeGraphEval 2026. The system fine-tunes a single ARBERTv2 encoder on inputs with typed entity markers that expose entity boundaries and types, also classifies each entity pair from the encoder{'}s classification token (\texttt{[CLS]}) state concatenated with the states at the subject and object start markers. It is trained over the full 41-label space, including \textit{no-relation}, with focal loss, class-balanced sampling, label smoothing, and \textit{no-relation} down-weighting to counter severe class imbalance. Our submission ranked third of seven teams with a micro-F1 of 0.9332, 0.23 points above the official baseline, which fine-tunes the same encoder. We also report an analysis of seed-dependent behaviour on fine-grained geopolitical relations from our earlier development experiments and discuss the limitations of our evaluation."
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<abstract>In this paper, we present a relation extraction system developed for Subtask 2 of KnowledgeGraphEval 2026. The system fine-tunes a single ARBERTv2 encoder on inputs with typed entity markers that expose entity boundaries and types, also classifies each entity pair from the encoder’s classification token ([CLS]) state concatenated with the states at the subject and object start markers. It is trained over the full 41-label space, including no-relation, with focal loss, class-balanced sampling, label smoothing, and no-relation down-weighting to counter severe class imbalance. Our submission ranked third of seven teams with a micro-F1 of 0.9332, 0.23 points above the official baseline, which fine-tunes the same encoder. We also report an analysis of seed-dependent behaviour on fine-grained geopolitical relations from our earlier development experiments and discuss the limitations of our evaluation.</abstract>
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%0 Conference Proceedings
%T ArmyForRE at KnowledgeGraphEval 2026: Typed Entity Markers and Imbalance-Aware Training for Arabic Relation Extraction
%A Shoumi, Milyun Ni’ma
%A Syulistyo, Arie Rachmad
%A Azizah, Aulia Resty
%A Putra, Savero Athallah Hardiana
%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 shoumi-etal-2026-armyforre
%X In this paper, we present a relation extraction system developed for Subtask 2 of KnowledgeGraphEval 2026. The system fine-tunes a single ARBERTv2 encoder on inputs with typed entity markers that expose entity boundaries and types, also classifies each entity pair from the encoder’s classification token ([CLS]) state concatenated with the states at the subject and object start markers. It is trained over the full 41-label space, including no-relation, with focal loss, class-balanced sampling, label smoothing, and no-relation down-weighting to counter severe class imbalance. Our submission ranked third of seven teams with a micro-F1 of 0.9332, 0.23 points above the official baseline, which fine-tunes the same encoder. We also report an analysis of seed-dependent behaviour on fine-grained geopolitical relations from our earlier development experiments and discuss the limitations of our evaluation.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.158/
%P 1255-1260
Markdown (Informal)
[ArmyForRE at KnowledgeGraphEval 2026: Typed Entity Markers and Imbalance-Aware Training for Arabic Relation Extraction](https://aclanthology.org/2026.arabicnlp-sharedtasks.158/) (Shoumi et al., ArabicNLP 2026)
ACL