论文标题
您如何与分析聊天机器人交谈？重新访问用于设计分析对话行为的Gricean Maxims
How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational Behavior
论文作者
论文摘要
聊天机器人引起了各种任务的对话界面的兴趣。尽管存在用于聊天机器人界面的一般设计准则，但Line Work探索了支持与数据对话的分析聊天机器人。我们探索Gricean Maxims，以帮助有效的对话互动的基本设计。我们还从自然语言界面中汲取灵感来探索数据，以支持歧义和意图处理。我们与30名参与者进行了OZ研究的向导，以评估用户对文本和语音聊天机器人设计变体的期望。结果确定了意图解释的偏好，并根据接口提供的用户期望揭示了偏好。随后，我们对三个分析聊天机器人系统（文本 +图表，语音 +图表，仅语音）进行了探索性分析，该系统实现了这些首选的设计变体。第二个参与者研究的经验证据为数据驱动的对话（例如解释意图，数据取向以及通过适当的系统响应建立信任）提供了特定的含义。
Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interaction. We also draw inspiration from natural language interfaces for data exploration to support ambiguity and intent handling. We ran Wizard of Oz studies with 30 participants to evaluate user expectations for text and voice chatbot design variants. Results identified preferences for intent interpretation and revealed variations in user expectations based on the interface affordances. We subsequently conducted an exploratory analysis of three analytical chatbot systems (text + chart, voice + chart, voice-only) that implement these preferred design variants. Empirical evidence from a second 30-participant study informs implications specific to data-driven conversation such as interpreting intent, data orientation, and establishing trust through appropriate system responses.
