  Singleton mentions, i.e.~entities mentioned only once in a text, are
important to how humans understand discourse from a theoretical perspective.
However previous attempts to incorporate their detection in end-to-end neural
coreference resolution for English have been hampered by the lack of singleton
mention spans in the OntoNotes benchmark. This paper addresses this limitation
by combining predicted mentions from existing nested NER systems and features
derived from OntoNotes syntax trees. With this approach, we create a near
approximation of the OntoNotes dataset with all singleton mentions, achieving
~94% recall on a sample of gold singletons. We then propose a two-step neural
mention and coreference resolution system, named SPLICE, and compare its
performance to the end-to-end approach in two scenarios: the OntoNotes test set
and the out-of-domain (OOD) OntoGUM corpus. Results indicate that reconstructed
singleton training yields results comparable to end-to-end systems for
OntoNotes, while improving OOD stability (+1.1 avg. F1). We conduct error
analysis for mention detection and delve into its impact on coreference
clustering, revealing that precision improvements deliver more substantial
benefits than increases in recall for resolving coreference chains.
