Probing for Dutch Relative Pronoun Choice

Abstract

We propose a linguistically motivated version of the relative pronoun probing task for Dutch (where a model has to predict whether a masked token is either die or dat), collect realistic data for it using a parsed corpus, and probe the performance of four context-sensitive bert-based neural language models. Whereas the original task, which simply masked all occurrences of the words die and dat, was relatively easy, the linguistically motivated task turns out to be much harder. Models differ considerably in their performance, but a monolingual model trained on a heterogeneous corpus appears to be most robust.