Natural Language Queries
Data is a prevalent part of every business and scientific domain, but its explosive volume and increasing complexity make data querying and exploration challenging even for experts. In an attempt to bridge the gap between users and data, text-to-SQL systems enable users to pose natural language queries over relational databases. We test their limits and build novel systems.
One of the biggest hurdles in today's exploration systems is that the system provides no explanations of the results or system choices. Nor does it trigger input from the user, for example, by asking the user to provide more information. We enable a conversational setting, wherethe system can explain results in natural language and can ask clarifications.
In a mixed-initiative setting, the system actively guides the user in what possible actions to perform or data to look at next. We are interested in recommendations in both cold-start (where the user has not given any input) and warm-start settings (where the user has asked one or more queries but may not know what to do next). In the formercase, the goal is to show a set of example or starter queries that the users could use to get some initial answers from the dataset. In the latter case, the system can leverage the users' interactions (queries) to show possible next queries.
As we increasingly depend on a variety of data-driven systems to assist us in many aspects of life, such as search engines and recommendation systems, we need to think about the fairness of such systems.