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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - AraFinNLP 2024
T2 - The First Arabic Financial NLP Shared Task
AU - Malaysha, Sanad
AU - El-Haj, Mo
AU - Ezzini, Saad
AU - Khalilia, Mohammed
AU - Jarrar, Mustafa
AU - Almujaiwel, Sultan
AU - Berrada, Ismail
AU - Bouamor, Houda
PY - 2024/8/16
Y1 - 2024/8/16
N2 - The expanding financial markets of the Arab world require sophisticated Arabic NLP tools. To address this need within the banking domain, the Arabic Financial NLP (AraFinNLP) shared task proposes two subtasks: (i) Multi-dialect Intent Detection and (ii) Cross-dialect Translation and Intent Preservation. This shared task uses the updated ArBanking77 dataset, which includes about 39k parallel queries in MSA and four dialects. Each query is labeled with one or more of a common 77 intents in the banking domain. These resources aim to foster the development of robust financial Arabic NLP, particularly in the areas of machine translation and banking chat-bots. A total of 45 unique teams registered for this shared task, with 11 of them actively participated in the test phase. Specifically, 11 teams participated in Subtask 1, while only 1 team participated in Subtask 2. The winning team of Subtask 1 achieved F1 score of 0.8773, and the only team submitted in Subtask 2 achieved a 1.667 BLEU score.
AB - The expanding financial markets of the Arab world require sophisticated Arabic NLP tools. To address this need within the banking domain, the Arabic Financial NLP (AraFinNLP) shared task proposes two subtasks: (i) Multi-dialect Intent Detection and (ii) Cross-dialect Translation and Intent Preservation. This shared task uses the updated ArBanking77 dataset, which includes about 39k parallel queries in MSA and four dialects. Each query is labeled with one or more of a common 77 intents in the banking domain. These resources aim to foster the development of robust financial Arabic NLP, particularly in the areas of machine translation and banking chat-bots. A total of 45 unique teams registered for this shared task, with 11 of them actively participated in the test phase. Specifically, 11 teams participated in Subtask 1, while only 1 team participated in Subtask 2. The winning team of Subtask 1 achieved F1 score of 0.8773, and the only team submitted in Subtask 2 achieved a 1.667 BLEU score.
KW - Computer Science - Computation and Language
U2 - 10.18653/v1/2024.arabicnlp-1.34
DO - 10.18653/v1/2024.arabicnlp-1.34
M3 - Conference contribution/Paper
SP - 393
EP - 402
BT - Proceedings of The Second Arabic Natural Language Processing Conference
A2 - Habash, Nizar
PB - Association for Computational Linguistics (ACL Anthology)
CY - Kerrville, Texas
ER -