Stack-propagation: Improved Representation Learning for Syntax

Understand

Traditional syntax models typically leverage part-of-speech (POS) information by constructing features from hand-tuned templates.

• We demonstrate that a better approach is to utilize POS tags as a regularizer of learned representations.
• We propose a simple method for learning a stacked pipeline of models which we call "stack-propagation".
• We apply this to dependency parsing and tagging, where we use the hidden layer of the tagger network as a representation of the input tokens for the parser.