  Neural sequence labelling approaches have achieved state of the art results
in morphological tagging. We evaluate the efficacy of four standard sequence
labelling models on Sanskrit, a morphologically rich, fusional Indian language.
As its label space can theoretically contain more than 40,000 labels, systems
that explicitly model the internal structure of a label are more suited for the
task, because of their ability to generalise to labels not seen during
training. We find that although some neural models perform better than others,
one of the common causes for error for all of these models is mispredictions
due to syncretism.
