TY - GEN
T1 - NADI 2024
T2 - 2nd Arabic Natural Language Processing Conference, ArabicNLP 2024
AU - Abdul-Mageed, Muhammad
AU - Keleg, Amr
AU - Elmadany, Abdel Rahim
AU - Zhang, Chiyu
AU - Hamed, Injy
AU - Magdy, Walid
AU - Bouamor, Houda
AU - Habash, Nizar
N1 - Publisher Copyright:
©2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - We describe the findings of the fifth Nuanced Arabic Dialect Identification Shared Task (NADI 2024). NADI’s objective is to help advance SoTA Arabic NLP by providing guidance, datasets, modeling opportunities, and standardized evaluation conditions that allow researchers to collaboratively compete on prespecified tasks. NADI 2024 targeted both dialect identification cast as a multi-label task (Subtask 1), identification of the Arabic level of dialectness (Subtask 2), and dialect-to-MSA machine translation (Subtask 3). A total of 51 unique teams registered for the shared task, of whom 12 teams have participated (with 76 valid submissions during the test phase). Among these, three teams participated in Subtask 1, three in Subtask 2, and eight in Subtask 3. The winning teams achieved 50.57 F1 on Subtask 1, 0.1403 RMSE for Subtask 2, and 20.44 BLEU in Subtask 3, respectively. Results show that Arabic dialect processing tasks such as dialect identification and machine translation remain challenging. We describe the methods employed by the participating teams and briefly offer an outlook for NADI.
AB - We describe the findings of the fifth Nuanced Arabic Dialect Identification Shared Task (NADI 2024). NADI’s objective is to help advance SoTA Arabic NLP by providing guidance, datasets, modeling opportunities, and standardized evaluation conditions that allow researchers to collaboratively compete on prespecified tasks. NADI 2024 targeted both dialect identification cast as a multi-label task (Subtask 1), identification of the Arabic level of dialectness (Subtask 2), and dialect-to-MSA machine translation (Subtask 3). A total of 51 unique teams registered for the shared task, of whom 12 teams have participated (with 76 valid submissions during the test phase). Among these, three teams participated in Subtask 1, three in Subtask 2, and eight in Subtask 3. The winning teams achieved 50.57 F1 on Subtask 1, 0.1403 RMSE for Subtask 2, and 20.44 BLEU in Subtask 3, respectively. Results show that Arabic dialect processing tasks such as dialect identification and machine translation remain challenging. We describe the methods employed by the participating teams and briefly offer an outlook for NADI.
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M3 - Conference contribution
AN - SCOPUS:85199650852
T3 - ArabicNLP 2024 - 2nd Arabic Natural Language Processing Conference, Proceedings of the Conference
SP - 709
EP - 728
BT - ArabicNLP 2024 - 2nd Arabic Natural Language Processing Conference, Proceedings of the Conference
A2 - Habash, Nizar
A2 - Bouamor, Houda
A2 - Eskander, Ramy
A2 - Tomeh, Nadi
A2 - Farha, Ibrahim Abu
A2 - Abdelali, Ahmed
A2 - Touileb, Samia
A2 - Hamed, Injy
A2 - Onaizan, Yaser
A2 - Alhafni, Bashar
A2 - Antoun, Wissam
A2 - Khalifa, Salam
A2 - Haddad, Hatem
A2 - Zitouni, Imed
A2 - AlKhamissi, Badr
A2 - Almatham, Rawan
A2 - Mrini, Khalil
PB - Association for Computational Linguistics (ACL)
Y2 - 16 August 2024
ER -