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
- Editors:
- Yusuke Miyao, Kenji Sagae
- Venue:
- IWPT
- SIG:
- SIGPARSE
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 105–114
- Language:
- URL:
- https://aclanthology.org/W17-6315
- DOI:
- 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, IWPT 2017)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/W17-6315.pdf
- Code
- LeonCrashCode/Encoder-Decoder-Parser