Learning to Compose Words into Sentences with Reinforcement Learning

Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling

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.

Conflicts: google.com