  Previous work on using BiLSTM models for PoS tagging has primarily focused on
small tagsets. We evaluate BiLSTM models for tagging Icelandic, a
morphologically rich language, using a relatively large tagset. Our baseline
BiLSTM model achieves higher accuracy than any previously published tagger not
taking advantage of a morphological lexicon. When we extend the model by
incorporating such data, we outperform previous state-of-the-art results by a
significant margin. We also report on work in progress that attempts to address
the problem of data sparsity inherent in morphologically detailed, fine-grained
tagsets. We experiment with training a separate model on only the lexical
category and using the coarse-grained output tag as an input for the main
model. This method further increases the accuracy and reduces the tagging
errors by 21.3% compared to previous state-of-the-art results. Finally, we
train and test our tagger on a new gold standard for Icelandic.
