  Morphological analysis involves predicting the syntactic traits of a word
(e.g. {POS: Noun, Case: Acc, Gender: Fem}). Previous work in morphological
tagging improves performance for low-resource languages (LRLs) through
cross-lingual training with a high-resource language (HRL) from the same
family, but is limited by the strict, often false, assumption that tag sets
exactly overlap between the HRL and LRL. In this paper we propose a method for
cross-lingual morphological tagging that aims to improve information sharing
between languages by relaxing this assumption. The proposed model uses
factorial conditional random fields with neural network potentials, making it
possible to (1) utilize the expressive power of neural network representations
to smooth over superficial differences in the surface forms, (2) model pairwise
and transitive relationships between tags, and (3) accurately generate tag sets
that are unseen or rare in the training data. Experiments on four languages
from the Universal Dependencies Treebank demonstrate superior tagging
accuracies over existing cross-lingual approaches.
