  Despite achieving encouraging results, neural Referring Expression Generation
models are often thought to lack transparency. We probed neural Referential
Form Selection (RFS) models to find out to what extent the linguistic features
influencing the RE form are learnt and captured by state-of-the-art RFS models.
The results of 8 probing tasks show that all the defined features were learnt
to some extent. The probing tasks pertaining to referential status and
syntactic position exhibited the highest performance. The lowest performance
was achieved by the probing models designed to predict discourse structure
properties beyond the sentence level.
