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Social media currently provide a window on our lives, making it possible tolearn how people from different places, with different backgrounds, ages, andgenders use language. In this work we exploit a newly-created Arabic datasetwith ground truth age and gender labels to learn these attributes bothindividually and in a multi-task setting at the sentence level. Our models arebased on variations of deep bidirectional neural networks. More specifically,we build models with gated recurrent units and bidirectional encoderrepresentations from transformers (BERT). We show the utility of multi-tasklearning (MTL) on the two tasks and identify task-specific attention as asuperior choice in this context. We also find that a single-task BERT modeloutperform our best MTL models on the two tasks. We report tweet-level accuracyof 51.43% for the age task (three-way) and 65.30% on the gender task (binary),both of which outperforms our baselines with a large margin. Our models arelanguage-agnostic, and so can be applied to other languages.

Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media