  Natural, spontaneous dialogue proceeds incrementally on a word-by-word basis;
and it contains many sorts of disfluency such as mid-utterance/sentence
hesitations, interruptions, and self-corrections. But training data for machine
learning approaches to dialogue processing is often either cleaned-up or wholly
synthetic in order to avoid such phenomena. The question then arises of how
well systems trained on such clean data generalise to real spontaneous
dialogue, or indeed whether they are trainable at all on naturally occurring
dialogue data. To answer this question, we created a new corpus called bAbI+ by
systematically adding natural spontaneous incremental dialogue phenomena such
as restarts and self-corrections to the Facebook AI Research's bAbI dialogues
dataset. We then explore the performance of a state-of-the-art retrieval model,
MemN2N, on this more natural dataset. Results show that the semantic accuracy
of the MemN2N model drops drastically; and that although it is in principle
able to learn to process the constructions in bAbI+, it needs an impractical
amount of training data to do so. Finally, we go on to show that an
incremental, semantic parser -- DyLan -- shows 100% semantic accuracy on both
bAbI and bAbI+, highlighting the generalisation properties of linguistically
informed dialogue models.
