  Dialog acts can be interpreted as the atomic units of a conversation, more
fine-grained than utterances, characterized by a specific communicative
function. The ability to structure a conversational transcript as a sequence of
dialog acts -- dialog act recognition, including the segmentation -- is
critical for understanding dialog. We apply two pre-trained transformer models,
XLNet and Longformer, to this task in English and achieve strong results on
Switchboard Dialog Act and Meeting Recorder Dialog Act corpora with dialog act
segmentation error rates (DSER) of 8.4% and 14.2%. To understand the key
factors affecting dialog act recognition, we perform a comparative analysis of
models trained under different conditions. We find that the inclusion of a
broader conversational context helps disambiguate many dialog act classes,
especially those infrequent in the training data. The presence of punctuation
in the transcripts has a massive effect on the models' performance, and a
detailed analysis reveals specific segmentation patterns observed in its
absence. Finally, we find that the label set specificity does not affect dialog
act segmentation performance. These findings have significant practical
implications for spoken language understanding applications that depend heavily
on a good-quality segmentation being available.
