Representation Learning and Dynamic Programming for Arc-Hybrid Parsing

Joseph Le Roux, Antoine Rozenknop, Mathieu Lacroix


Abstract
We present a new method for transition-based parsing where a solution is a pair made of a dependency tree and a derivation graph describing the construction of the former. From this representation we are able to derive an efficient parsing algorithm and design a neural network that learns vertex representations and arc scores. Experimentally, although we only train via local classifiers, our approach improves over previous arc-hybrid systems and reach state-of-the-art parsing accuracy.
Anthology ID:
K19-1023
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
238–248
Language:
URL:
https://www.aclweb.org/anthology/K19-1023
DOI:
10.18653/v1/K19-1023
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PDF:
http://aclanthology.lst.uni-saarland.de/K19-1023.pdf