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.
@article{arxiv.1701.01623,
title = {Cross-Lingual Dependency Parsing with Late Decoding for Truly Low-Resource Languages},
author = {Michael Sejr Schlichtkrull and Anders Søgaard},
journal= {arXiv preprint arXiv:1701.01623},
year = {2017}
}