The authors define a universal dialogue act schema, align several task-oriented dialogue datasets to it, and report a tagger reaching 54.1% F1 unsupervised and 57.7% semi-supervised on human-human dialogues.
DAs have been investi- gated by dialogue researchers for many years [2] and multi- ple taxonomies have been proposed [3, 4, 5] (see [6] for a review)
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Towards Universal Dialogue Act Tagging for Task-Oriented Dialogues
The authors define a universal dialogue act schema, align several task-oriented dialogue datasets to it, and report a tagger reaching 54.1% F1 unsupervised and 57.7% semi-supervised on human-human dialogues.