论文标题
比较饮食信息域中NLG聊天机器人与图形应用程序的信息性
Comparing informativeness of an NLG chatbot vs graphical app in diet-information domain
论文作者
论文摘要
图表和表格之类的数据的可视化表示对于读者来说可能是具有挑战性的。先前的工作表明，将可视化与文本相结合可以改善静态环境中见解的沟通，但对交互式的见解知之甚少。在这项工作中，我们提出了一个NLG聊天机器人，该聊天机器人可以处理自然语言查询，并通过图表和文本的结合提供见解。我们将其应用于营养，域通信质量至关重要。通过人群评估，我们将聊天机器人的信息性与传统的静态饮食应用程序进行了比较。我们发现，对话环境可显着提高用户对各种任务中饮食数据的理解，并且用户认为聊天机器人比传统应用程序更有用和快速使用。
Visual representation of data like charts and tables can be challenging to understand for readers. Previous work showed that combining visualisations with text can improve the communication of insights in static contexts, but little is known about interactive ones. In this work we present an NLG chatbot that processes natural language queries and provides insights through a combination of charts and text. We apply it to nutrition, a domain communication quality is critical. Through crowd-sourced evaluation we compare the informativeness of our chatbot against traditional, static diet-apps. We find that the conversational context significantly improved users' understanding of dietary data in various tasks, and that users considered the chatbot as more useful and quick to use than traditional apps.
