Learning to compose words into sentences with reinforcement learning

Journal article

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: Yogatama, D; Blunsom, P; Dyer, C; Grefenstette, E; Ling, W

• Publication status: Not published
• Peer review status: Reviewed (other)
• Publisher: International Conference on Learning Representations
• Journal: 5th International Conference on Learning Representations (ICLR 2017)
• Publication date: 2017-04-01
• Acceptance date: 2017-02-06
• Keywords: cs.CL