Question Generation from a Knowledge Base.

When designing the natural language question asking interface for a formal knowledge base, managing and scoping the user expectations regarding what questions the system can answer is a key challenge. Allowing users to type ask arbitrary English questions will likely result in user frustration, because the system may be unable to answer many questions even if it correctly understands the natural language phrasing. We present a technique for responding to natural language questions, by suggesting a series of questions that the system can actually answer. We also show that the suggested questions are useful in a variety of ways in an intelligent textbook to improve student learning.