  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
pre-specified 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 F\textsubscript{1} 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.
