论文标题
检查对话系统的用户评论：Alexa技能的案例研究
Examining user reviews of conversational systems: a case study of Alexa skills
论文作者
论文摘要
对话系统使用口头语言与用户互动。尽管诸如Amazon Alexa之类的会话系统变得普遍且具有有趣的功能，但对于这些系统的使用者所面临的问题鲜为人知。 在本文中，我们研究了2800多个Alexa技能的用户评论，以了解其中提出的评论和问题的特征。我们的结果表明，大多数技能都会收到少于50个评论。我们对使用开放编码的用户评论进行的定性研究导致在用户评论中识别16种类型的问题。与内容，与在线服务和设备集成有关的问题，错误和回归是用户提出的主要问题。与更传统的移动应用程序相比，我们的结果还表明用户的数量和投诉类型差异。我们讨论了结果对从业者和研究人员的含义。
Conversational systems use spoken language to interact with their users. Although conversational systems, such as Amazon Alexa, are becoming common and afford interesting functionalities, there is little known about the issues users of these systems face. In this paper, we study user reviews of more than 2,800 Alexa skills to understand the characteristics of the reviews and issues that are raised in them. Our results suggest that most skills receive less than 50 reviews. Our qualitative study of user reviews using open coding resulted in identifying 16 types of issues in the user reviews. Issues related to the content, integration with online services and devices, error, and regression are top issues raised by the users. Our results also indicate differences in volume and types of complaints by users when compared with more traditional mobile applications. We discuss the implication of our results for practitioners and researchers.
