  We propose reconstruction probing, a new analysis method for contextualized
representations based on reconstruction probabilities in masked language models
(MLMs). This method relies on comparing the reconstruction probabilities of
tokens in a given sequence when conditioned on the representation of a single
token that has been fully contextualized and when conditioned on only the
decontextualized lexical prior of the model. This comparison can be understood
as quantifying the contribution of contextualization towards reconstruction --
the difference in the reconstruction probabilities can only be attributed to
the representational change of the single token induced by contextualization.
We apply this analysis to three MLMs and find that contextualization boosts
reconstructability of tokens that are close to the token being reconstructed in
terms of linear and syntactic distance. Furthermore, we extend our analysis to
finer-grained decomposition of contextualized representations, and we find that
these boosts are largely attributable to static and positional embeddings at
the input layer.
