@inproceedings{hossain-2026-axiom-alexandriax,
title = "Axiom at {A}lexandria{X}-2026: Confidence-Thresholded Dialectal Encoder Fine-Tuning for {MT} Error Span Detection and Classification",
author = "Hossain, Md. Ajwad",
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.100/",
pages = "800--805",
ISBN = "979-8-89176-497-2",
abstract = "This paper describes the ``Axiom'' team{'}s submission to the AlexandriaX-2026 Shared Task, focusing on Subtask 3: MT Error Detection and Classification. We frame the task of predicting fine-grained error spans and their corresponding LQM categories as a joint token classification problem using a BIO tagging scheme. Evaluating multiple Arabic encoders, we find that dialect-specific pretraining significantly outperforms generative extraction baselines. To address severe label imbalance across error categories, we apply a square-root scaled inverse-frequency class-weighted loss. Furthermore, we introduce a confidence-thresholded decoding strategy at inference to reduce false-positive span over-prediction. Our final system achieved highly competitive performance, ranking 5th on the official leaderboard. We highlight that while overlap-based detection is robust, exact span matching remains a persistent challenge due to the non-standardized nature of dialectal Arabic orthography."
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<abstract>This paper describes the “Axiom” team’s submission to the AlexandriaX-2026 Shared Task, focusing on Subtask 3: MT Error Detection and Classification. We frame the task of predicting fine-grained error spans and their corresponding LQM categories as a joint token classification problem using a BIO tagging scheme. Evaluating multiple Arabic encoders, we find that dialect-specific pretraining significantly outperforms generative extraction baselines. To address severe label imbalance across error categories, we apply a square-root scaled inverse-frequency class-weighted loss. Furthermore, we introduce a confidence-thresholded decoding strategy at inference to reduce false-positive span over-prediction. Our final system achieved highly competitive performance, ranking 5th on the official leaderboard. We highlight that while overlap-based detection is robust, exact span matching remains a persistent challenge due to the non-standardized nature of dialectal Arabic orthography.</abstract>
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%0 Conference Proceedings
%T Axiom at AlexandriaX-2026: Confidence-Thresholded Dialectal Encoder Fine-Tuning for MT Error Span Detection and Classification
%A Hossain, Md. Ajwad
%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 hossain-2026-axiom-alexandriax
%X This paper describes the “Axiom” team’s submission to the AlexandriaX-2026 Shared Task, focusing on Subtask 3: MT Error Detection and Classification. We frame the task of predicting fine-grained error spans and their corresponding LQM categories as a joint token classification problem using a BIO tagging scheme. Evaluating multiple Arabic encoders, we find that dialect-specific pretraining significantly outperforms generative extraction baselines. To address severe label imbalance across error categories, we apply a square-root scaled inverse-frequency class-weighted loss. Furthermore, we introduce a confidence-thresholded decoding strategy at inference to reduce false-positive span over-prediction. Our final system achieved highly competitive performance, ranking 5th on the official leaderboard. We highlight that while overlap-based detection is robust, exact span matching remains a persistent challenge due to the non-standardized nature of dialectal Arabic orthography.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.100/
%P 800-805
Markdown (Informal)
[Axiom at AlexandriaX-2026: Confidence-Thresholded Dialectal Encoder Fine-Tuning for MT Error Span Detection and Classification](https://aclanthology.org/2026.arabicnlp-sharedtasks.100/) (Hossain, ArabicNLP 2026)
ACL