Neural Arabic singular-to-plural conversion using a pretrained Character-BERT and a fused transformer
Azzam Radman,
Mohammed Atros,
Rehab Duwairi
Abstract:Morphological re-inflection generation is one of the most challenging tasks in the natural language processing (NLP) domain, especially with morphologically rich, low-resource languages like Arabic. In this research, we investigate the ability of transformer-based models in the singular-to-plural Arabic noun conversion task. We start with pretraining a Character-BERT model on a masked language modeling task using 1,134,950 Arabic words and then adopting the fusion technique to transfer the knowledge gained by … Show more
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