Semantically driven auto-completion

Abstract

The Bloomberg Terminal has been a leading source of financial data and analytics for over 30 years. Through its thousands of functions, the Terminal allows its users to query and run analytics over a large array of data sources, including structured, semi-structured, and unstructured data; as well as plot charts, set up event-driven alerts and triggers, create interactive maps, exchange information via email and instant messaging, and so on. To improve user experience, we have been building question answering systems that can understand a wide range of natural language constructs for various domains that are of fundamental interest to our users. Such natural language interfaces, while exceedingly helpful to users, introduce a number of usability challenges of their own. We tackle some of these challenges through auto-completion. A distinguishing mark of our auto-complete systems is that they are based on and guided by corresponding semantic parsing systems. We describe the autocomplete problem as it arises in this setting, the novel algorithms that we use to solve it, and report on the quality of the results and the efficiency of our approach.