  This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared
task on Multilingual Parsing from Raw Text to Universal Dependencies. We base
our submission on Stanford's winning system for the CoNLL 2017 shared task and
make two effective extensions: 1) incorporating deep contextualized word
embeddings into both the part of speech tagger and parser; 2) ensembling
parsers trained with different initialization. We also explore different ways
of concatenating treebanks for further improvements. Experimental results on
the development data show the effectiveness of our methods. In the final
evaluation, our system was ranked first according to LAS (75.84%) and
outperformed the other systems by a large margin.
