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