Every day, millions of people use natural-language interfaces such as Siri, Google Now, Cortana, Alexa and others via in-home devices, phones, or messaging channels such as Messenger, Slack, and Skype, among others. The goal of this workshop is to advance the use of conversational natural language to query structured databases.
Building natural language interfaces for databases has been one of the "holy grails" of the database community since the early days of databases. In recent years, considerable progress has been made in this direction. Conversational interfaces are even more challenging since they need to keep track of context and deal with human short cuts.
Given the immense importance of such interfaces and the challenges that lie ahead in making such systems widely useful, building conversational interfaces over data has attracted interest from several areas: databases, machine learning, natural language processing and understanding and human-computer interaction forming a rich space of solutions.
The goal of this workshop is to bring together researchers and practitioners in this area, to clarify impactful research problems, share findings from large-scale real-world deployments, and generate new ideas for future lines of research. We seek papers that address any aspect of any of these issues.
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Organizers and Chairs
- H.V Jagadish, University of Michigan
- Georgia Koutrika, Athena Research Center
- Fatma Ozcan, IBM Research