  Previous work on Neural Referring Expression Generation (REG) all uses
WebNLG, an English dataset that has been shown to reflect a very limited range
of referring expression (RE) use. To tackle this issue, we build a dataset
based on the OntoNotes corpus that contains a broader range of RE use in both
English and Chinese (a language that uses zero pronouns). We build neural
Referential Form Selection (RFS) models accordingly, assess them on the dataset
and conduct probing experiments. The experiments suggest that, compared to
WebNLG, OntoNotes is better for assessing REG/RFS models. We compare English
and Chinese RFS and confirm that, in line with linguistic theories, Chinese RFS
depends more on discourse context than English.
