Shift-Reduce Constituent Parsing with Neural Lookahead Features

Jiangming Liu, Yue Zhang


Abstract
Transition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a parser stack, which consists of a sequence of non-local constituents. On the other hand, during incremental parsing, constituent information on the right hand side of the current word is not utilized, which is a relative weakness of shift-reduce parsing. To address this limitation, we leverage a fast neural model to extract lookahead features. In particular, we build a bidirectional LSTM model, which leverages full sentence information to predict the hierarchy of constituents that each word starts and ends. The results are then passed to a strong transition-based constituent parser as lookahead features. The resulting parser gives 1.3% absolute improvement in WSJ and 2.3% in CTB compared to the baseline, giving the highest reported accuracies for fully-supervised parsing.
Anthology ID:
Q17-1004
Volume:
Transactions of the Association for Computational Linguistics, Volume 5
Month:
Year:
2017
Address:
Cambridge, MA
Editors:
Lillian Lee, Mark Johnson, Kristina Toutanova
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
45–58
Language:
URL:
https://aclanthology.org/Q17-1004
DOI:
10.1162/tacl_a_00045
Bibkey:
Cite (ACL):
Jiangming Liu and Yue Zhang. 2017. Shift-Reduce Constituent Parsing with Neural Lookahead Features. Transactions of the Association for Computational Linguistics, 5:45–58.
Cite (Informal):
Shift-Reduce Constituent Parsing with Neural Lookahead Features (Liu & Zhang, TACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/Q17-1004.pdf
Code
 SUTDNLP/LookAheadConparser