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In this paper we explore deep learning models with memory component orattention mechanism for question answering task. We combine and compare threemodels, Neural Machine Translation, Neural Turing Machine, and Memory Networksfor a simulated QA data set. This paper is the first one that uses NeuralMachine Translation and Neural Turing Machines for solving QA tasks. Ourresults suggest that the combination of attention and memory have potential tosolve certain QA problem.

Empirical Study on Deep Learning Models for Question Answering