  We describe and evaluate different approaches to the conversion of gold
standard corpus data from Stanford Typed Dependencies (SD) and Penn-style
constituent trees to the latest English Universal Dependencies representation
(UD 2.2). Our results indicate that pure SD to UD conversion is highly accurate
across multiple genres, resulting in around 1.5% errors, but can be improved
further to fewer than 0.5% errors given access to annotations beyond the pure
syntax tree, such as entity types and coreference resolution, which are
necessary for correct generation of several UD relations. We show that
constituent-based conversion using CoreNLP (with automatic NER) performs
substantially worse in all genres, including when using gold constituent trees,
primarily due to underspecification of phrasal grammatical functions.
