  Text normalization is an important enabling technology for several NLP tasks.
Recently, neural-network-based approaches have outperformed well-established
models in this task. However, in languages other than English, there has been
little exploration in this direction. Both the scarcity of annotated data and
the complexity of the language increase the difficulty of the problem. To
address these challenges, we use a sequence-to-sequence model with
character-based attention, which in addition to its self-learned character
embeddings, uses word embeddings pre-trained with an approach that also models
subword information. This provides the neural model with access to more
linguistic information especially suitable for text normalization, without
large parallel corpora. We show that providing the model with word-level
features bridges the gap for the neural network approach to achieve a
state-of-the-art F1 score on a standard Arabic language correction shared task
dataset.
