title: Learning to Compose Words into Sentences with Reinforcement Learning
abstract: We use reinforcement learning to learn
  tree-structured neural networks for computing representations of natural language sentences.
  In contrast with prior work on tree-structured models, in which the trees are either provided as input or
  predicted using supervision from explicit treebank annotations,
  the tree structures in this work are optimized to improve performance on a downstream task.
  Experiments demonstrate the benefit of
  learning task-specific composition orders, outperforming both sequential encoders and recursive encoders based on treebank annotations.
  We analyze the induced trees and show that while they discover
  some linguistically intuitive structures (e.g., noun phrases, simple verb phrases),
  they are different than conventional English syntactic structures.
authors: Dani Yogatama Phil Blunsom Chris Dyer Edward Grefenstette Wang Ling
hIndex: Chris Dyer 73