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arXiv:1601.01705cs.CL

Learning to Compose Neural Networks for Question Answering

Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein

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.

arXiv comments
Journal reference
DOI
Subjects
cs.CLComputation and Language cs.CVComputer Vision and Pattern Recognition cs.NENeural and Evolutionary Computing

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Linked by 2 identifiable commenters in 2 comments across 2 threads, from Jul 2017 to Dec 2020. Found in 1 HN submission from May 2016 to May 2016. Open submission discussions.

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Discussed in comments2 threads

  1. Ask HN: What's the best paper you've read in 2020? 1 comment

    • Discovering Symbolic Models from Deep Learning with Inductive Biases [1] trains graph neural nets on astrophysical phenomena and then performs symbolic regression to generate algebraic formulae to elegantly model the phenomena in a classical physics framework. It's largely gone…

      KhoomeiKView on HNParent context

  2. When not to use deep learning 1 comment

    • Yes. I think these architectures are very exciting and a step in the "right" direction. Eventually we will want to move from rote memorization and pattern matching to more challenging aspects of intelligence. https://arxiv.org/abs/1601.01705v4 (Learning to Compose Neural…

      daddyoView on HNParent context

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