  Parsing spoken dialogue poses unique difficulties, including disfluencies and
unmarked boundaries between sentence-like units. Previous work has shown that
prosody can help with parsing disfluent speech (Tran et al. 2018), but has
assumed that the input to the parser is already segmented into sentence-like
units (SUs), which isn't true in existing speech applications. We investigate
how prosody affects a parser that receives an entire dialogue turn as input (a
turn-based model), instead of gold standard pre-segmented SUs (an SU-based
model). In experiments on the English Switchboard corpus, we find that when
using transcripts alone, the turn-based model has trouble segmenting SUs,
leading to worse parse performance than the SU-based model. However, prosody
can effectively replace gold standard SU boundaries: with prosody, the
turn-based model performs as well as the SU-based model (90.79 vs. 90.65 F1
score, respectively), despite performing two tasks (SU segmentation and
parsing) rather than one (parsing alone). Analysis shows that pitch and
intensity features are the most important for this corpus, since they allow the
model to correctly distinguish an SU boundary from a speech disfluency -- a
distinction that the model otherwise struggles to make.
