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First-order factoid question answering assumes that the question can beanswered by a single fact in a knowledge base (KB). While this does not seemlike a challenging task, many recent attempts that apply either complexlinguistic reasoning or deep neural networks achieve 65%-76% accuracy onbenchmark sets. Our approach formulates the task as two machine learningproblems: detecting the entities in the question, and classifying the questionas one of the relation types in the KB. We train a recurrent neural network tosolve each problem. On the SimpleQuestions dataset, our approach yieldssubstantial improvements over previously published results --- even neuralnetworks based on much more complex architectures. The simplicity of ourapproach also has practical advantages, such as efficiency and modularity, thatare valuable especially in an industry setting. In fact, we present apreliminary analysis of the performance of our model on real queries fromComcast's X1 entertainment platform with millions of users every day.

No Need to Pay Attention: Simple Recurrent Neural Networks Work! (for Answering "Simple" Questions)