  How can an end-user provide feedback if a deployed structured prediction
model generates inconsistent output, ignoring the structural complexity of
human language? This is an emerging topic with recent progress in synthetic or
constrained settings, and the next big leap would require testing and tuning
models in real-world settings. We present a new dataset, Interscript,
containing user feedback on a deployed model that generates complex everyday
tasks. Interscript contains 8,466 data points -- the input is a possibly
erroneous script and a user feedback, and the output is a modified script. We
posit two use-cases of \ours that might significantly advance the
state-of-the-art in interactive learning. The dataset is available at:
https://github.com/allenai/interscript.
