  Spontaneous spoken dialogue is often disfluent, containing pauses,
hesitations, self-corrections and false starts. Processing such phenomena is
essential in understanding a speaker's intended meaning and controlling the
flow of the conversation. Furthermore, this processing needs to be word-by-word
incremental to allow further downstream processing to begin as early as
possible in order to handle real spontaneous human conversational behaviour.
  In addition, from a developer's point of view, it is highly desirable to be
able to develop systems which can be trained from `clean' examples while also
able to generalise to the very diverse disfluent variations on the same data --
thereby enhancing both data-efficiency and robustness. In this paper, we
present a multi-task LSTM-based model for incremental detection of disfluency
structure, which can be hooked up to any component for incremental
interpretation (e.g. an incremental semantic parser), or else simply used to
`clean up' the current utterance as it is being produced.
  We train the system on the Switchboard Dialogue Acts (SWDA) corpus and
present its accuracy on this dataset. Our model outperforms prior neural
network-based incremental approaches by about 10 percentage points on SWDA
while employing a simpler architecture. To test the model's generalisation
potential, we evaluate the same model on the bAbI+ dataset, without any
additional training. bAbI+ is a dataset of synthesised goal-oriented dialogues
where we control the distribution of disfluencies and their types. This shows
that our approach has good generalisation potential, and sheds more light on
which types of disfluency might be amenable to domain-general processing.
