  Diacritic restoration has gained importance with the growing need for
machines to understand written texts. The task is typically modeled as a
sequence labeling problem and currently Bidirectional Long Short Term Memory
(BiLSTM) models provide state-of-the-art results. Recently, Bai et al. (2018)
show the advantages of Temporal Convolutional Neural Networks (TCN) over
Recurrent Neural Networks (RNN) for sequence modeling in terms of performance
and computational resources. As diacritic restoration benefits from both
previous as well as subsequent timesteps, we further apply and evaluate a
variant of TCN, Acausal TCN (A-TCN), which incorporates context from both
directions (previous and future) rather than strictly incorporating previous
context as in the case of TCN. A-TCN yields significant improvement over TCN
for diacritization in three different languages: Arabic, Yoruba, and
Vietnamese. Furthermore, A-TCN and BiLSTM have comparable performance, making
A-TCN an efficient alternative over BiLSTM since convolutions can be trained in
parallel. A-TCN is significantly faster than BiLSTM at inference time
(270%-334% improvement in the amount of text diacritized per minute).
