Neural Morphological Tagging of Lemma Sequences for Machine Translation

摘要

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%.