A Pre-trained BERT Model for Arabic Author Profiling
Chiyu Zhang, Muhammad Abdul-Mageed
Journal of E - Technology, Vol. 11, No. 2, 2020.
DOI: https://doi.org/10.6025/jet/2020/11/2/54-59
Link: http://www.dline.info/jet/fulltext/v11n2/jetv11n2_2.pdf
Abstract:
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) [32].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.1