  We investigate how encoder-decoder models trained on a synthetic dataset of
task-oriented dialogues process disfluencies, such as hesitations and
self-corrections. We find that, contrary to earlier results, disfluencies have
very little impact on the task success of seq-to-seq models with attention.
Using visualisation and diagnostic classifiers, we analyse the representations
that are incrementally built by the model, and discover that models develop
little to no awareness of the structure of disfluencies. However, adding
disfluencies to the data appears to help the model create clearer
representations overall, as evidenced by the attention patterns the different
models exhibit.
