Neural Morphological Tagging of Lemma Sequences for Machine Translation
Costanza Conforti, Matthias Huck, Alexander Fraser
AMTA 2018

Translation to morphologically rich languages is a difficult task
because of sparsity caused by morphological richness. In this work we
perform a pilot study on predicting the morphologically rich POS tags
of sequences of lemmas. Similar studies have been conducted in the
context of phrase-based statistical machine translation. We implement
a state-of-the-art tagger taking lemmas as input and show that we can
successfully predict the morphologically rich POS tags, with
accuracies of up to 91%.