Learning from Emotions, Demographic Information and Implicit User
  Feedback in Task-Oriented Document-Grounded Dialogues
Dominic Petrak Thy Thy Tran Iryna Gurevych
The success of task-oriented and document-grounded dialogue systems depends
on users accepting and enjoying using them. To achieve this, recently published
work in the field of Human-Computer Interaction suggests that the combination
of considering demographic information, user emotions and learning from the
implicit feedback in their utterances, is particularly important. However,
these findings have not yet been transferred to the field of Natural Language
Processing, where these data are primarily studied separately. Accordingly, no
sufficiently annotated dataset is available. To address this gap, we introduce
FEDI, the first English dialogue dataset for task-oriented document-grounded
dialogues annotated with demographic information, user emotions and implicit
feedback. Our experiments with FLAN-T5, GPT-2 and LLaMA-2 show that these data
have the potential to improve task completion and the factual consistency of
the generated responses and user acceptance.