  The ability to recognise emotions lends a conversational artificial
intelligence a human touch. While emotions in chit-chat dialogues have received
substantial attention, emotions in task-oriented dialogues remain largely
unaddressed. This is despite emotions and dialogue success having equally
important roles in a natural system. Existing emotion-annotated task-oriented
corpora are limited in size, label richness, and public availability, creating
a bottleneck for downstream tasks. To lay a foundation for studies on emotions
in task-oriented dialogues, we introduce EmoWOZ, a large-scale manually
emotion-annotated corpus of task-oriented dialogues. EmoWOZ is based on
MultiWOZ, a multi-domain task-oriented dialogue dataset. It contains more than
11K dialogues with more than 83K emotion annotations of user utterances. In
addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine
dialogues within the same set of domains to sufficiently cover the space of
various emotions that can happen during the lifetime of a data-driven dialogue
system. To the best of our knowledge, this is the first large-scale open-source
corpus of its kind. We propose a novel emotion labelling scheme, which is
tailored to task-oriented dialogues. We report a set of experimental results to
show the usability of this corpus for emotion recognition and state tracking in
task-oriented dialogues.
