  We describe findings of the third Nuanced Arabic Dialect Identification
Shared Task (NADI 2022). NADI aims at advancing state of the art Arabic NLP,
including on Arabic dialects. It does so by affording diverse datasets and
modeling opportunities in a standardized context where meaningful comparisons
between models and approaches are possible. NADI 2022 targeted both dialect
identification (Subtask 1) and dialectal sentiment analysis (Subtask 2) at the
country level. A total of 41 unique teams registered for the shared task, of
whom 21 teams have actually participated (with 105 valid submissions). Among
these, 19 teams participated in Subtask 1 and 10 participated in Subtask 2. The
winning team achieved 27.06 F1 on Subtask 1 and F1=75.16 on Subtask 2,
reflecting that the two subtasks remain challenging and motivating future work
in this area. We describe methods employed by participating teams and offer an
outlook for NADI.
