  We report our models for detecting age, language variety, and gender from
social media data in the context of the Arabic author profiling and deception
detection shared task (APDA). We build simple models based on pre-trained
bidirectional encoders from transformers (BERT). We first fine-tune the
pre-trained BERT model on each of the three datasets with shared task released
data. Then we augment shared task data with in-house data for gender and
dialect, showing the utility of augmenting training data. Our best models on
the shared task test data are acquired with a majority voting of various BERT
models trained under different data conditions. We acquire 54.72% accuracy for
age, 93.75% for dialect, 81.67% for gender, and 40.97% joint accuracy across
the three tasks.
