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.
In this work, we investigated multi- ple annotated human-machine conversation datasets, with dif- ferences in DA schema
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.