  In this paper, we address the representation of coordinate constructions in
Enhanced Universal Dependencies (UD), where relevant dependency links are
propagated from conjunction heads to other conjuncts. English treebanks for
enhanced UD have been created from gold basic dependencies using a heuristic
rule-based converter, which propagates only core arguments. With the aim of
determining which set of links should be propagated from a semantic
perspective, we create a large-scale dataset of manually edited syntax graphs.
We identify several systematic errors in the original data, and propose to also
propagate adjuncts. We observe high inter-annotator agreement for this semantic
annotation task. Using our new manually verified dataset, we perform the first
principled comparison of rule-based and (partially novel) machine-learning
based methods for conjunction propagation for English. We show that learning
propagation rules is more effective than hand-designing heuristic rules. When
using automatic parses, our neural graph-parser based edge predictor
outperforms the currently predominant pipelinesusing a basic-layer tree parser
plus converters.
