  Incremental models that process sentences one token at a time will sometimes
encounter points where more than one interpretation is possible. Causal models
are forced to output one interpretation and continue, whereas models that can
revise may edit their previous output as the ambiguity is resolved. In this
work, we look at how restart-incremental Transformers build and update internal
states, in an effort to shed light on what processes cause revisions not viable
in autoregressive models. We propose an interpretable way to analyse the
incremental states, showing that their sequential structure encodes information
on the garden path effect and its resolution. Our method brings insights on
various bidirectional encoders for contextualised meaning representation and
dependency parsing, contributing to show their advantage over causal models
when it comes to revisions.
