  Neural morphological tagging has been regarded as an extension to POS tagging
task, treating each morphological tag as a monolithic label and ignoring its
internal structure. We propose to view morphological tags as composite labels
and explicitly model their internal structure in a neural sequence tagger. For
this, we explore three different neural architectures and compare their
performance with both CRF and simple neural multiclass baselines. We evaluate
our models on 49 languages and show that the neural architecture that models
the morphological labels as sequences of morphological category values performs
significantly better than both baselines establishing state-of-the-art results
in morphological tagging for most languages.

Computation and Language