Stack-propagation: Improved Representation Learning for Syntax

Yuan Zhang; David Weiss

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arXiv · 1603.06598

Stack-propagation: Improved Representation Learning for Syntax

Yuan Zhang·David Weiss

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Abstract

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. At test time, our parser does not require predicted POS tags. On 19 languages from the Universal Dependencies, our method is 1.3% (absolute) more accurate than a state-of-the-art graph-based approach and 2.7% more accurate than the most comparable greedy model.

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BibTeX RIS

Yuan Zhang, David Weiss. 2016-03-21. Stack-propagation: Improved Representation Learning for Syntax. https://arxiv.org/abs/1603.06598

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