Learning to Compose Neural Networks for Question Answering

Understand

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.