In-Order Transition-based Constituent Parsing

Jiangming Liu, Yue Zhang


Abstract
Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation tree, where bottom-up strategies and top-down strategies take post-order and pre-order traversal over trees, respectively. Bottom-up parsers benefit from rich features from readily built partial parses, but lack lookahead guidance in the parsing process; top-down parsers benefit from non-local guidance for local decisions, but rely on a strong encoder over the input to predict a constituent hierarchy before its construction. To mitigate both issues, we propose a novel parsing system based on in-order traversal over syntactic trees, designing a set of transition actions to find a compromise between bottom-up constituent information and top-down lookahead information. Based on stack-LSTM, our psycholinguistically motivated constituent parsing system achieves 91.8 F1 on the WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised reranking and 94.2 F1 with semi-supervised reranking, which are the best results on the WSJ benchmark.
Anthology ID:
Q17-1029
Volume:
Transactions of the Association for Computational Linguistics, Volume 5
Month:
Year:
2017
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
413–424
Language:
URL:
https://aclanthology.org/Q17-1029
DOI:
10.1162/tacl_a_00070
Bibkey:
Cite (ACL):
Jiangming Liu and Yue Zhang. 2017. In-Order Transition-based Constituent Parsing. Transactions of the Association for Computational Linguistics, 5:413–424.
Cite (Informal):
In-Order Transition-based Constituent Parsing (Liu & Zhang, TACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/Q17-1029.pdf
Code
 LeonCrashCode/InOrderParser +  additional community code
Data
Penn Treebank