论文标题
检测对话者的混乱位于人为对话：一项试点研究
Detecting Interlocutor Confusion in Situated Human-Avatar Dialogue: A Pilot Study
论文作者
论文摘要
为了提高与对话系统的参与水平，我们的长期研究目标旨在监测用户的混乱状态，并根据此类用户混乱状态调整对话策略。为此，在本文中，我们介绍了我们最初的研究以用户 - 阿瓦塔尔对话方案为中心，我们已经开发出来研究混乱的表现以及长期缓解的表现。我们提出了一个新的混乱定义，该定义特别适合针对任务对话的智能对话系统开发的要求。我们还介绍了基于OZ的向导数据收集方案的详细信息，其中用户与对话化头像进行了互动，并呈现了刺激，在某些情况下，这些刺激旨在调用用户中的困惑状态。还提供了此数据的研究分析。在这里，部署了三个预先训练的深度学习模型，以估计基本的情感，头部姿势和眼睛凝视。尽管有一个小的试点研究小组，但我们的分析表明这些指标与混乱状态之间存在显着关系。我们将这是对对话的语用学的自动分析中向前迈出的有用的一步。
In order to enhance levels of engagement with conversational systems, our long term research goal seeks to monitor the confusion state of a user and adapt dialogue policies in response to such user confusion states. To this end, in this paper, we present our initial research centred on a user-avatar dialogue scenario that we have developed to study the manifestation of confusion and in the long term its mitigation. We present a new definition of confusion that is particularly tailored to the requirements of intelligent conversational system development for task-oriented dialogue. We also present the details of our Wizard-of-Oz based data collection scenario wherein users interacted with a conversational avatar and were presented with stimuli that were in some cases designed to invoke a confused state in the user. Post study analysis of this data is also presented. Here, three pre-trained deep learning models were deployed to estimate base emotion, head pose and eye gaze. Despite a small pilot study group, our analysis demonstrates a significant relationship between these indicators and confusion states. We understand this as a useful step forward in the automated analysis of the pragmatics of dialogue.
