Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation

In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF) and Block-Normalized Gradient (BNG). The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models, which require language-dependent post-processing steps, unlike ours. Moreover, we show that diacritics in Arabic can be used to enhance the models of NLP tasks such as Machine Translation (MT) by proposing the Translation over Diacritization (ToD) approach.

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Results from the Paper

Ranked #2 on Arabic Text Diacritization on Tashkeela (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Arabic Text Diacritization Tashkeela Shakkelha Diacritic Error Rate 0.0169 # 2
Word Error Rate (WER) 0.0509 # 2


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