Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set (2017-08-01T00:00:00.000000Z)

TL;DR

A minimal feature set for transition-based dependency parsing is presented and the best unlabeled attachment score reported on the Chinese Treebank and the “second-best-in-class” result on the English Penn Treebank are achieved.

Abstract

We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Kuhlmann et al. (2011) to produce the first implementation of worst-case O(n^3) exact decoders for arc-hybrid and arc-eager transition systems. With our minimal features, we also present O(n^3) global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the “second-best-in-class” result on the English Penn Treebank.

Authors

Tianze Shi

3 papers

Liang Huang

2 papers

Lillian Lee

1 papers

TL;DR

Abstract

Authors

References66 items

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