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.

近期人机交互领域的研究表明，考虑到人口统计信息、用户情感以及从话语中的隐含反馈中学习是任务导向和以文档为基础的对话系统获得用户接受和喜欢的关键。然而，这些发现尚未在自然语言处理领域得到应用，缺乏相应的数据集。为了填补这一空白，我们引入了 FEDI，这是第一个包含任务导向的以文档为基础的对话的英语数据集，其中注释了人口统计信息、用户情感和隐含反馈。我们使用 FLAN-T5、GPT-2 和 LLaMA-2 进行实验，结果表明这些数据有潜力改善任务完成情况、生成回答的事实一致性以及用户接受程度。