Learning to Compose Neural Networks for Question Answering

Code

• github.com/jacobandreas/nmn2
  OfficialIn papernone★ 0
• github.com/abhigoyal1997/CS-763-Project
  pytorch★ 0
• github.com/RobertKirk/roam-tools
  none★ 0

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.

Tasks

Reproductions