An Investigation into an Always Listening Interface to Support Data Exploration
An Investigation into an Always Listening Interface to Support Data Exploration
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DOI：
10.1145/3581641.3584079
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发表时间：
2023-03
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通讯作者：
Roderick S. Tabalba;Nurit Kirshenbaum;J. Leigh;Abari Bhattacharya;Veronica Grosso;Barbara Di Eugenio;Andrew E. Johnson;Moira L. Zellner
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作者：
Roderick S. Tabalba;Nurit Kirshenbaum;J. Leigh;Abari Bhattacharya;Veronica Grosso;Barbara Di Eugenio;Andrew E. Johnson;Moira L. Zellner
促进数据探索任务的自然语言接口正在迅速获得研究界的兴趣，因为它们使用户能够将注意力集中在查询任务上，而不是图表构建的机制上。然而，当前系统仅依赖于处理用户的明确命令来生成用户的预期图表。这些命令可能是模糊的，由于自然语言的倾向，如语音不流利和欠规范。在本文中，我们开发并研究了一个始终侦听的接口如何帮助上下文不精确的查询。我们的研究表明，一个始终倾听的界面能够使用正在进行的对话来填补不精确命令的缺失属性，消除不准确命令的歧义而不要求用户澄清，以及在没有明确要求的情况下生成图表。
Natural Language Interfaces that facilitate data exploration tasks are rapidly gaining in interest in the research community because they enable users to focus their attention on the task of inquiry rather than the mechanics of chart construction. Yet, current systems rely solely on processing the user’s explicit commands to generate the user’s intended chart. These commands can be ambiguous due to natural language tendencies such as speech disfluency and underspecification. In this paper, we developed and studied how an always listening interface can help contextualize imprecise queries. Our study revealed that an always listening interface is able to use an on-going conversation to fill in missing properties for imprecise commands, disambiguate inaccurate commands without asking the user for clarification, as well as generate charts without being explicitly asked.