Transition-based Parsing with Lighter Feed-Forward Networks

David Vilares, Carlos Gómez-Rodríguez


Abstract
We explore whether it is possible to build lighter parsers, that are statistically equivalent to their corresponding standard version, for a wide set of languages showing different structures and morphologies. As testbed, we use the Universal Dependencies and transition-based dependency parsers trained on feed-forward networks. For these, most existing research assumes de facto standard embedded features and relies on pre-computation tricks to obtain speed-ups. We explore how these features and their size can be reduced and whether this translates into speed-ups with a negligible impact on accuracy. The experiments show that grand-daughter features can be removed for the majority of treebanks without a significant (negative or positive) LAS difference. They also show how the size of the embeddings can be notably reduced.
Anthology ID:
W18-6019
Volume:
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | UDW | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
162–172
Language:
URL:
https://www.aclweb.org/anthology/W18-6019
DOI:
10.18653/v1/W18-6019
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-6019.pdf