  In this article, we use probing to investigate phenomena that occur during
fine-tuning and knowledge distillation of a BERT-based natural language
understanding (NLU) model. Our ultimate purpose was to use probing to better
understand practical production problems and consequently to build better NLU
models. We designed experiments to see how fine-tuning changes the linguistic
capabilities of BERT, what the optimal size of the fine-tuning dataset is, and
what amount of information is contained in a distilled NLU based on a tiny
Transformer. The results of the experiments show that the probing paradigm in
its current form is not well suited to answer such questions. Structural, Edge
and Conditional probes do not take into account how easy it is to decode probed
information. Consequently, we conclude that quantification of information
decodability is critical for many practical applications of the probing
paradigm.
