"On a standard benchmark consisting of randomly drawn English newswire sentences (the 20 year old Penn Treebank), Parsey McParseface recovers individual dependencies between words with over 94% accuracy, beating our own previous state-of-the-art results, which were already better than any previous approach."
From the original paper, "Our model achieves state-of-the-art accuracy on all of these tasks, matching or outperforming LSTMs while being significantly faster. In particular for dependency parsing on the Wall Street Journal we achieve the best-ever published unlabeled attachment score of 94.41%."
This seems like a narrower standard than described, specifically being better at parsing the Penn Treebank than the best natural language parser for English on the Wall Street Journal.
The statistics listed on the project GitHub actually contradict these claims by showing the original March 2016 implementation has higher accuracy than Parsey McParseface.