AraNet: A Deep Learning Toolkit for Arabic Social Media

December 30, 2019 ยท Declared Dead ยท ๐Ÿ› OSACT

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Authors Muhammad Abdul-Mageed, Chiyu Zhang, Azadeh Hashemi, El Moatez Billah Nagoudi arXiv ID 1912.13072 Category cs.CL: Computation & Language Cross-listed cs.IR, cs.LG Citations 33 Venue OSACT Last Checked 4 months ago
Abstract
We describe AraNet, a collection of deep learning Arabic social media processing tools. Namely, we exploit an extensive host of publicly available and novel social media datasets to train bidirectional encoders from transformer models (BERT) to predict age, dialect, gender, emotion, irony, and sentiment. AraNet delivers state-of-the-art performance on a number of the cited tasks and competitively on others. In addition, AraNet has the advantage of being exclusively based on a deep learning framework and hence feature engineering free. To the best of our knowledge, AraNet is the first to performs predictions across such a wide range of tasks for Arabic NLP and thus meets a critical needs. We publicly release AraNet to accelerate research and facilitate comparisons across the different tasks.
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