In this paper, we introduce a unified model for both diacritic types while improving upon the state-of-the-art.
we approached the task as a sequence-to-sequence (seq2seq) problem ; taking advantage of the recent advancements in Neural Machine Translation (NMT) (Britz et al., 2017;Kuchaiev et al., 2018) among other applications where seq2seq models made a breakthrough (Yu et al., 2016;Witten et al., 2016;Abadi et al., 2016).