  Part-of-Speech (POS) tagging is an important component of the NLP pipeline,
but many low-resource languages lack labeled data for training. An established
method for training a POS tagger in such a scenario is to create a labeled
training set by transferring from high-resource languages. In this paper, we
propose a novel method for transferring labels from multiple high-resource
source to low-resource target languages. We formalize POS tag projection as
graph-based label propagation. Given translations of a sentence in multiple
languages, we create a graph with words as nodes and alignment links as edges
by aligning words for all language pairs. We then propagate node labels from
source to target using a Graph Neural Network augmented with transformer
layers. We show that our propagation creates training sets that allow us to
train POS taggers for a diverse set of languages. When combined with enhanced
contextualized embeddings, our method achieves a new state-of-the-art for
unsupervised POS tagging of low-resource languages.
