Arabic Diacritization with Recurrent Neural Networks
Yonatan Belinkov, James Glass · 2015
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.