Auto-completion for Question Answering Systems at Bloomberg.

The Bloomberg Terminal is the leading source of information and news in the finance industry. Through hundreds of functions that provide access to a vast wealth of structured and semi-structured data, the terminal is able to satisfy a wide range of information needs. Users can find what they need by constructing queries, plotting charts, creating alerts, and so on. Until recently, most queries to the terminal were constructed through dedicated GUIs. For instance, if users wanted to screen for technology companies that met certain criteria, they would specify the criteria by filling out a form via a sequence of interactions with GUI elements such as drop-down lists, checkboxes, radio and toggle buttons, etc. To facilitate information retrieval in the terminal, we are equipping it with the ability to understand and answer queries expressed in natural language. Our QA (question answering) systems map structurally complex questions like the above to a logical meaning representation which can then be translated to an executable query language (such as SQL or SPARQL). At that point we can execute the queries against a suitable back end, obtain the results, and present them to the users. Adding a natural-language interface to a data repository introduces usability challenges of its own, chief amongst them being this: How can the user know what the system can and cannot understand and answer (without needing to undergo extensive training)? We can unpack this question into two separate parts: 1) How can we convey the full range of the system's abilities? 2) How can we convey its limitations? We use auto-complete as a tool to help meet both challenges. Specifically, the first question pertains to the general issue of discoverability: We want at least some of the suggested completions to act as vehicles for discovering data and functionality of which users may have not been previously aware. The second question pertains to expectation management. Naturally, no QA system can attain perfect performance; limiting factors include representational shortcomings and various kinds of incompleteness of the underlying data sources, as well as NLP technology limitations. We want to stop generating completions as a signal indicating that we are not able to understand and/or answer what is being typed.