Neural Morphological Tagging of Lemma Sequences for Machine Translation
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%
AMTA