Shared Task on Dialect Identification

[Salam Khalifa]

Hello All,

We are pleased to announce the Nuanced Arabic Dialect Identification (NADI) shared task as part of the Workshop on Arabic NLP WANLP2022.

Summary: Arabic has a widely varying collection of dialects. Many of these dialects remain under-studied due to the rarity of resources. The goal of the Nuanced Arabic Dialect Identification (NADI) shared task series is to alleviate this bottleneck by providing datasets and modeling opportunities for participants to carry out dialect identification. Dialect identification is the task of automatically detecting the source variety of a given text or speech segment. In addition to nuanced dialect identification at the country level, NADI 2022 also offers a new subtask focused on country-level sentiment analysis. While we invite participation in either of the two subtasks, we hope that teams will submit systems to both tasks (i.e., participate in the two tasks rather than only one task). By offering two subtasks, we hope to receive systems that exploit diverse machine learning architectures. This could include multi-task learning systems as well as sequence-to-sequence architectures in a single model such as the text-to-text Transformer. Other approaches could also be possible. We introduce the two subtasks next.

Organizers: Muhammad Abdul-Mageed, Chiyu Zhang, Abdelrahim Elmadany, Nizar Habash, and Houda Bouamor.

To find more information about the shared task please visit: https://nadi.dlnlp.ai/

Looking forward to your participation,

Salam Khalifa (On behalf of the WANLP publicity chairs)

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Salam Khalifa

PhD Student

Linguistics Department

Institute for Advanced Computational Science (IACS)

Stony Brook University

salam....@stonybrook.edu