  In cross-lingual dependency annotation projection, information is often lost
during transfer because of early decoding. We present an end-to-end graph-based
neural network dependency parser that can be trained to reproduce matrices of
edge scores, which can be directly projected across word alignments. We show
that our approach to cross-lingual dependency parsing is not only simpler, but
also achieves an absolute improvement of 2.25% averaged across 10 languages
compared to the previous state of the art.
