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.

翻译：任务导向型与文档对话系统的成功依赖于用户的接受度和使用意愿。为实现这一目标，人机交互领域的最新研究表明，结合人口统计信息、用户情感，并学习其话语中的隐式反馈至关重要。然而，这些发现尚未被迁移至自然语言处理领域——在该领域中，此类数据主要被分开研究，因此缺乏充分标注的数据集。为填补这一空白，我们提出了FEDI——首个面向任务导向型文档对话的英文对话数据集，对人口统计信息、用户情感及隐式反馈进行了标注。基于FLAN-T5、GPT-2和LLaMA-2的实验表明，这些数据具备提升任务完成度、增强生成回复的事实一致性及用户接受度的潜力。