  First-order factoid question answering assumes that the question can be
answered by a single fact in a knowledge base (KB). While this does not seem
like a challenging task, many recent attempts that apply either complex
linguistic reasoning or deep neural networks achieve 65%-76% accuracy on
benchmark sets. Our approach formulates the task as two machine learning
problems: detecting the entities in the question, and classifying the question
as one of the relation types in the KB. We train a recurrent neural network to
solve each problem. On the SimpleQuestions dataset, our approach yields
substantial improvements over previously published results --- even neural
networks based on much more complex architectures. The simplicity of our
approach also has practical advantages, such as efficiency and modularity, that
are valuable especially in an industry setting. In fact, we present a
preliminary analysis of the performance of our model on real queries from
Comcast's X1 entertainment platform with millions of users every day.
