Revising with a Backward Glance: Regressions and Skips during Reading as
  Cognitive Signals for Revision Policies in Incremental Processing
Brielen Madureira Pelin Çelikkol David Schlangen
In NLP, incremental processors produce output in instalments, based on
incoming prefixes of the linguistic input. Some tokens trigger revisions,
causing edits to the output hypothesis, but little is known about why models
revise when they revise. A policy that detects the time steps where revisions
should happen can improve efficiency. Still, retrieving a suitable signal to
train a revision policy is an open problem, since it is not naturally available
in datasets. In this work, we investigate the appropriateness of regressions
and skips in human reading eye-tracking data as signals to inform revision
policies in incremental sequence labelling. Using generalised mixed-effects
models, we find that the probability of regressions and skips by humans can
potentially serve as useful predictors for revisions in BiLSTMs and Transformer
models, with consistent results for various languages.