Conversations with data: Advancing the state of the art in language-driven data exploration
One key aspiration of AI is to develop natural and effective task-oriented conversational systems. Task-oriented conversational systems use a natural language interface to collaborate with and support people in accomplishing specific goals and activities. They go beyond chitchat conversation. For example, as personal digital assistants, they ease the stress of trip planning or reduce the expertise required to generate a sales report from a database. While natural language understanding (NLU) technology and research have achieved remarkable recent progress, task-oriented assistance requires tackling additional challenges in practical NLU.
Consider a prime application of task-oriented conversations: language-driven data exploration. Data scientists, analysts, and information workers routinely spend more than half of their time exploring, visualizing, and reformatting datasets, according to Anaconda’s “The