  Dialogue act recognition is an important part of natural language
understanding. We investigate the way dialogue act corpora are annotated and
the learning approaches used so far. We find that the dialogue act is
context-sensitive within the conversation for most of the classes.
Nevertheless, previous models of dialogue act classification work on the
utterance-level and only very few consider context. We propose a novel
context-based learning method to classify dialogue acts using a character-level
language model utterance representation, and we notice significant improvement.
We evaluate this method on the Switchboard Dialogue Act corpus, and our results
show that the consideration of the preceding utterances as a context of the
current utterance improves dialogue act detection.
