  Morphologically rich languages often lack the annotated linguistic resources
required to develop accurate natural language processing tools. We propose
models suitable for training morphological taggers with rich tagsets for
low-resource languages without using direct supervision. Our approach extends
existing approaches of projecting part-of-speech tags across languages, using
bitext to infer constraints on the possible tags for a given word type or
token. We propose a tagging model using Wsabie, a discriminative
embedding-based model with rank-based learning. In our evaluation on 11
languages, on average this model performs on par with a baseline
weakly-supervised HMM, while being more scalable. Multilingual experiments show
that the method performs best when projecting between related language pairs.
Despite the inherently lossy projection, we show that the morphological tags
predicted by our models improve the downstream performance of a parser by +0.6
LAS on average.
