  We present a machine learning approach that ranked on the first place in the
Arabic Dialect Identification (ADI) Closed Shared Tasks of the 2018 VarDial
Evaluation Campaign. The proposed approach combines several kernels using
multiple kernel learning. While most of our kernels are based on character
p-grams (also known as n-grams) extracted from speech or phonetic transcripts,
we also use a kernel based on dialectal embeddings generated from audio
recordings by the organizers. In the learning stage, we independently employ
Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR).
Preliminary experiments indicate that KRR provides better classification
results. Our approach is shallow and simple, but the empirical results obtained
in the 2018 ADI Closed Shared Task prove that it achieves the best performance.
Furthermore, our top macro-F1 score (58.92%) is significantly better than the
second best score (57.59%) in the 2018 ADI Shared Task, according to the
statistical significance test performed by the organizers. Nevertheless, we
obtain even better post-competition results (a macro-F1 score of 62.28%) using
the audio embeddings released by the organizers after the competition. With a
very similar approach (that did not include phonetic features), we also ranked
first in the ADI Closed Shared Tasks of the 2017 VarDial Evaluation Campaign,
surpassing the second best method by 4.62%. We therefore conclude that our
multiple kernel learning method is the best approach to date for Arabic dialect
identification.
