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How can we enable computers to automatically answer questions like "Whocreated the character Harry Potter"? Carefully built knowledge bases providerich sources of facts. However, it remains a challenge to answer factoidquestions raised in natural language due to numerous expressions of onequestion. In particular, we focus on the most common questions --- ones thatcan be answered with a single fact in the knowledge base. We propose CFO, aConditional Focused neural-network-based approach to answering factoidquestions with knowledge bases. Our approach first zooms in a question to findmore probable candidate subject mentions, and infers the final answers with aunified conditional probabilistic framework. Powered by deep recurrent neuralnetworks and neural embeddings, our proposed CFO achieves an accuracy of 75.7%on a dataset of 108k questions - the largest public one to date. It outperformsthe current state of the art by an absolute margin of 11.8%.

CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases