  We study the problem of analyzing tweets with Universal Dependencies. We
extend the UD guidelines to cover special constructions in tweets that affect
tokenization, part-of-speech tagging, and labeled dependencies. Using the
extended guidelines, we create a new tweet treebank for English (Tweebank v2)
that is four times larger than the (unlabeled) Tweebank v1 introduced by Kong
et al. (2014). We characterize the disagreements between our annotators and
show that it is challenging to deliver consistent annotation due to ambiguity
in understanding and explaining tweets. Nonetheless, using the new treebank, we
build a pipeline system to parse raw tweets into UD. To overcome annotation
noise without sacrificing computational efficiency, we propose a new method to
distill an ensemble of 20 transition-based parsers into a single one. Our
parser achieves an improvement of 2.2 in LAS over the un-ensembled baseline and
outperforms parsers that are state-of-the-art on other treebanks in both
accuracy and speed.
