Arabic, Hebrew, and similar languages are typically written without diacritics, leading to ambiguity and posing a major challenge for core language processing tasks like speech recognition. Previous approaches to automatic diacritization employed a variety of machine learning techniques. However, they typically rely on existing tools like morphological analyzers and therefore cannot be easily extended to new genres and languages. We develop a recurrent neural network with long shortterm memory layers for predicting diacritics in Arabic text. Our language-independent approach is trained solely from diacritized text without relying on external tools. We show experimentally that our model can rival state-of-the-art methods that have access to additional resources.
Another language-independent approach is based on a sequence classification and uses a recurrent Neural Network with memory layers to restore diacritics for Arabic REF .
Arabic Diacritization with Recurrent Neural Networks
EMNLP
Computer Science