Neural machine translation for automated feedback on children's
  early-stage writing
Jonas Vestergaard Jensen Mikkel Jordahn Michael Riis Andersen
In this work, we address the problem of assessing and constructing feedback
for early-stage writing automatically using machine learning. Early-stage
writing is typically vastly different from conventional writing due to phonetic
spelling and lack of proper grammar, punctuation, spacing etc. Consequently,
early-stage writing is highly non-trivial to analyze using common linguistic
metrics. We propose to use sequence-to-sequence models for "translating"
early-stage writing by students into "conventional" writing, which allows the
translated text to be analyzed using linguistic metrics. Furthermore, we
propose a novel robust likelihood to mitigate the effect of noise in the
dataset. We investigate the proposed methods using a set of numerical
experiments and demonstrate that the conventional text can be predicted with
high accuracy.