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 module network, achieves state-of-theart results on benchmark datasets in both visual and structured domains.
In REF , authors use RL to learn the parameters of QA model for both images and structured knowledge bases.
Learning to Compose Neural Networks for Question Answering
ArXiv
ArXiv
Computer Science