  Peeking into the inner workings of BERT has shown that its layers resemble
the classical NLP pipeline, with progressively more complex tasks being
concentrated in later layers. To investigate to what extent these results also
hold for a language other than English, we probe a Dutch BERT-based model and
the multilingual BERT model for Dutch NLP tasks. In addition, through a deeper
analysis of part-of-speech tagging, we show that also within a given task,
information is spread over different parts of the network and the pipeline
might not be as neat as it seems. Each layer has different specialisations, so
that it may be more useful to combine information from different layers,
instead of selecting a single one based on the best overall performance.
