  Machine learning approaches for building task-oriented dialogue systems
require large conversational datasets with labels to train on. We are
interested in building task-oriented dialogue systems from human-human
conversations, which may be available in ample amounts in existing customer
care center logs or can be collected from crowd workers. Annotating these
datasets can be prohibitively expensive. Recently multiple annotated
task-oriented human-machine dialogue datasets have been released, however their
annotation schema varies across different collections, even for well-defined
categories such as dialogue acts (DAs). We propose a Universal DA schema for
task-oriented dialogues and align existing annotated datasets with our schema.
Our aim is to train a Universal DA tagger (U-DAT) for task-oriented dialogues
and use it for tagging human-human conversations. We investigate multiple
datasets, propose manual and automated approaches for aligning the different
schema, and present results on a target corpus of human-human dialogues. In
unsupervised learning experiments we achieve an F1 score of 54.1% on system
turns in human-human dialogues. In a semi-supervised setup, the F1 score
increases to 57.7% which would otherwise require at least 1.7K manually
annotated turns. For new domains, we show further improvements when unlabeled
or labeled target domain data is available.
