Encoder-Decoder Shift-Reduce Syntactic Parsing

Jiangming Liu, Yue Zhang


Abstract
Encoder-decoder neural networks have been used for many NLP tasks, such as neural machine translation. They have also been applied to constituent parsing by using bracketed tree structures as a target language, translating input sentences into syntactic trees. A more commonly used method to linearize syntactic trees is the shift-reduce system, which uses a sequence of transition-actions to build trees. We empirically investigate the effectiveness of applying the encoder-decoder network to transition-based parsing. On standard benchmarks, our system gives comparable results to the stack LSTM parser for dependency parsing, and significantly better results compared to the aforementioned parser for constituent parsing, which uses bracketed tree formats.
Anthology ID:
W17-6315
Volume:
Proceedings of the 15th International Conference on Parsing Technologies
Month:
September
Year:
2017
Address:
Pisa, Italy
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
105–114
Language:
URL:
https://aclanthology.org/W17-6315
DOI:
Bibkey:
Cite (ACL):
Jiangming Liu and Yue Zhang. 2017. Encoder-Decoder Shift-Reduce Syntactic Parsing. In Proceedings of the 15th International Conference on Parsing Technologies, pages 105–114, Pisa, Italy. Association for Computational Linguistics.
Cite (Informal):
Encoder-Decoder Shift-Reduce Syntactic Parsing (Liu & Zhang, 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-6315.pdf
Code
 LeonCrashCode/Encoder-Decoder-Parser
Data
Penn Treebank