Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset

Guanyi Chen, Fahime Same, Kees van Deemter

Abstract: 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 both languages BERT has the highest performance. Also, our results suggest that in line with linguistic theories, Chinese RFS depends more on discourse context than English.