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Learning to Compose Neural Networks for Question Answering
release_jrrrrp5v5nab5hpium5t3tazki
by
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein
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as a article
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2016
Abstract
We describe a question answering model that applies to both images and
structured knowledge bases. The model uses natural language strings to
automatically assemble neural networks from a collection of composable modules.
Parameters for these modules are learned jointly with network-assembly
parameters via reinforcement learning, with only (world, question, answer)
triples as supervision. Our approach, which we term a dynamic neural model
network, achieves state-of-the-art results on benchmark datasets in both visual
and structured domains.
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1601.01705v2
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