Papers › D-Nikud: Enhancing Hebrew Diacritization with LSTM and Pretrained Models
D-Nikud: Enhancing Hebrew Diacritization with LSTM and Pretrained Models
Adi Rosenthal, Nadav Shaked
D-Nikud, a novel approach to Hebrew diacritization that integrates the strengths of LSTM networks and BERT-based (transformer) pre-trained model. Inspired by the methodologies employed in Nakdimon, we integrate it with the TavBERT pre-trained model, our system incorporates advanced architectural choices and diverse training data. Our experiments showcase state-of-the-art results on several benchmark datasets, with a particular emphasis on modern texts and more specified diacritization like gender.
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