  We develop neural morphological tagging and disambiguation models for
Estonian. First, we experiment with two neural architectures for morphological
tagging - a standard multiclass classifier which treats each morphological tag
as a single unit, and a sequence model which handles the morphological tags as
sequences of morphological category values. Secondly, we complement these
models with the analyses generated by a rule-based Estonian morphological
analyser (MA) VABAMORF , thus performing a soft morphological disambiguation.
We compare two ways of supplementing a neural morphological tagger with the MA
outputs: firstly, by adding the combined analyses embeddings to the word
representation input to the neural tagging model, and secondly, by adopting an
attention mechanism to focus on the most relevant analyses generated by the MA.
Experiments on three Estonian datasets show that our neural architectures
consistently outperform the non-neural baselines, including HMM-disambiguated
VABAMORF, while augmenting models with MA outputs results in a further
performance boost for both models.
