Simple Question Answering by Attentive Convolutional Neural Network
This work focuses on answering single relation factoid questions over Freebase. Each question can acquire the answer from a single fact of form (subject, predicate, object) in Freebase. This task, simple question answering (SimpleQA) can be addressed via a two step pipeline: entity linking and fact selection. In fact selection, we match the subject entity in a fact candidate with the entity mention in the question by a character level convolutional neural network (char CNN) and match the predicate in that fact with the question by a word level CNN (word CNN) This work makes two main contributions. (i) A simple and effective entity linker over Freebase is proposed. Our entity linker outperforms the state of the art entity linker over SimpleQA task. (ii) A novel attentive maxpooling is stacked over word CNN, so that the predicate representation can be matched with the predicate focused question representation more effectively. Experiments show that our system sets new state of the art in this task.
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