@inproceedings{chi-2026-phoenix,
title = "Phoenix at {A}ra{G}enre 2026: Transductive Bias Correction and Multi-View Label Representations for Zero-Shot {A}rabic Genre Classification",
author = "Chi, Nguyen Minh",
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.31/",
pages = "261--265",
ISBN = "979-8-89176-497-2",
abstract = "AraGenre 2026 requires hierarchical classification of Arabic texts into evaluation-specific genres unseen during training, using supplied English definitions and taxonomy information. We present a transductive zero-shot system combining multilingual retrieval over multi-view label representations with per-genre z-normalization, calibrated Qwen3-8B candidate reranking, sibling-focused reranking, metric-aware macro-F1 decoding, and margin-gated shortlist widening. On the official 27,972-instance test set, the system achieves 0.5800 Hierarchical Macro F1, comprising 0.749 broad and 0.411 specific macro F1. Per-genre normalization provides the largest build-up improvement (+0.095), while an encoder-only diagnostic attributes +0.082 to LLM reranking. Calibrated log-probability fusion, multi-view labels, and decoding refinements provide further complementary gains."
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<abstract>AraGenre 2026 requires hierarchical classification of Arabic texts into evaluation-specific genres unseen during training, using supplied English definitions and taxonomy information. We present a transductive zero-shot system combining multilingual retrieval over multi-view label representations with per-genre z-normalization, calibrated Qwen3-8B candidate reranking, sibling-focused reranking, metric-aware macro-F1 decoding, and margin-gated shortlist widening. On the official 27,972-instance test set, the system achieves 0.5800 Hierarchical Macro F1, comprising 0.749 broad and 0.411 specific macro F1. Per-genre normalization provides the largest build-up improvement (+0.095), while an encoder-only diagnostic attributes +0.082 to LLM reranking. Calibrated log-probability fusion, multi-view labels, and decoding refinements provide further complementary gains.</abstract>
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%0 Conference Proceedings
%T Phoenix at AraGenre 2026: Transductive Bias Correction and Multi-View Label Representations for Zero-Shot Arabic Genre Classification
%A Chi, Nguyen Minh
%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 chi-2026-phoenix
%X AraGenre 2026 requires hierarchical classification of Arabic texts into evaluation-specific genres unseen during training, using supplied English definitions and taxonomy information. We present a transductive zero-shot system combining multilingual retrieval over multi-view label representations with per-genre z-normalization, calibrated Qwen3-8B candidate reranking, sibling-focused reranking, metric-aware macro-F1 decoding, and margin-gated shortlist widening. On the official 27,972-instance test set, the system achieves 0.5800 Hierarchical Macro F1, comprising 0.749 broad and 0.411 specific macro F1. Per-genre normalization provides the largest build-up improvement (+0.095), while an encoder-only diagnostic attributes +0.082 to LLM reranking. Calibrated log-probability fusion, multi-view labels, and decoding refinements provide further complementary gains.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.31/
%P 261-265
Markdown (Informal)
[Phoenix at AraGenre 2026: Transductive Bias Correction and Multi-View Label Representations for Zero-Shot Arabic Genre Classification](https://aclanthology.org/2026.arabicnlp-sharedtasks.31/) (Chi, ArabicNLP 2026)
ACL