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
- Editors:
- Lillian Lee, Mark Johnson, Kristina Toutanova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 413–424
- Language:
- URL:
- https://aclanthology.org/Q17-1029
- DOI:
- 10.1162/tacl_a_00070
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/Q17-1029.pdf
- Code
- LeonCrashCode/InOrderParser + additional community code
- Data
- Penn Treebank