@inproceedings{atou-etal-2026-lynx,
title = "Lynx at the {NADI} 2026 Shared Task: Exploring {ASR} Representations and Transcripts for Dialectal {A}rabic Speech Understanding",
author = "Atou, Houdaifa and
Yahia, Issam Ait and
Berrada, Ismail",
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.13/",
pages = "118--124",
ISBN = "979-8-89176-497-2",
abstract = "Speech conveys complementary information through its linguistic content and acoustic characteristics. Speech-related tasks can exploit either source of information or combine them to improve performance. In this paper, we present \textit{\textbf{Lynx{\_}Id}} and \textit{\textbf{Lynx{\_}In}}, our systems for the \textit{\textbf{NADI 2026}} Spoken Dialect Identification and Intent Recognition tasks, respectively. Both systems build on an automatic speech recognition (ASR) model. \textit{Lynx{\_}Id} is a late-fusion system for dialect identification. It combines internal representations from the ASR encoder with predictions derived from the ASR transcript. \textit{Lynx{\_}In} is a two-branch text-based system that uses ASR transcriptions for intent recognition. One branch predicts the intent among the known classes, while the second acts as a gate to detect out-of-scope utterances. \textit{Lynx{\_}Id} achieved 53.53{\%} accuracy and ranked third in the Spoken Dialect Identification task, while \textit{Lynx{\_}In} achieved a weighted F1 score of 76.86{\%} and ranked first in the Intent Recognition task."
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<abstract>Speech conveys complementary information through its linguistic content and acoustic characteristics. Speech-related tasks can exploit either source of information or combine them to improve performance. In this paper, we present Lynx_Id and Lynx_In, our systems for the NADI 2026 Spoken Dialect Identification and Intent Recognition tasks, respectively. Both systems build on an automatic speech recognition (ASR) model. Lynx_Id is a late-fusion system for dialect identification. It combines internal representations from the ASR encoder with predictions derived from the ASR transcript. Lynx_In is a two-branch text-based system that uses ASR transcriptions for intent recognition. One branch predicts the intent among the known classes, while the second acts as a gate to detect out-of-scope utterances. Lynx_Id achieved 53.53% accuracy and ranked third in the Spoken Dialect Identification task, while Lynx_In achieved a weighted F1 score of 76.86% and ranked first in the Intent Recognition task.</abstract>
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%0 Conference Proceedings
%T Lynx at the NADI 2026 Shared Task: Exploring ASR Representations and Transcripts for Dialectal Arabic Speech Understanding
%A Atou, Houdaifa
%A Yahia, Issam Ait
%A Berrada, Ismail
%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 atou-etal-2026-lynx
%X Speech conveys complementary information through its linguistic content and acoustic characteristics. Speech-related tasks can exploit either source of information or combine them to improve performance. In this paper, we present Lynx_Id and Lynx_In, our systems for the NADI 2026 Spoken Dialect Identification and Intent Recognition tasks, respectively. Both systems build on an automatic speech recognition (ASR) model. Lynx_Id is a late-fusion system for dialect identification. It combines internal representations from the ASR encoder with predictions derived from the ASR transcript. Lynx_In is a two-branch text-based system that uses ASR transcriptions for intent recognition. One branch predicts the intent among the known classes, while the second acts as a gate to detect out-of-scope utterances. Lynx_Id achieved 53.53% accuracy and ranked third in the Spoken Dialect Identification task, while Lynx_In achieved a weighted F1 score of 76.86% and ranked first in the Intent Recognition task.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.13/
%P 118-124
Markdown (Informal)
[Lynx at the NADI 2026 Shared Task: Exploring ASR Representations and Transcripts for Dialectal Arabic Speech Understanding](https://aclanthology.org/2026.arabicnlp-sharedtasks.13/) (Atou et al., ArabicNLP 2026)
ACL