  We propose a novel architecture for labelling character sequences that
achieves state-of-the-art results on the Tashkeela Arabic diacritization
benchmark. The core is a two-level recurrence hierarchy that operates on the
word and character levels separately---enabling faster training and inference
than comparable traditional models. A cross-level attention module further
connects the two, and opens the door for network interpretability. The task
module is a softmax classifier that enumerates valid combinations of
diacritics. This architecture can be extended with a recurrent decoder that
optionally accepts priors from partially diacritized text, which improves
results. We employ extra tricks such as sentence dropout and majority voting to
further boost the final result. Our best model achieves a WER of 5.34%,
outperforming the previous state-of-the-art with a 30.56% relative error
reduction.
