Abstract
We introduce novel dynamic oracles for training two of the most accurate known shift-reduce algorithms for constituent parsing: the top-down and in-order transition-based parsers. In both cases, the dynamic oracles manage to notably increase their accuracy, in comparison to that obtained by performing classic static training. In addition, by improving the performance of the state-of-the-art in-order shift-reduce parser, we achieve the best accuracy to date (92.0 F1) obtained by a fully-supervised single-model greedy shift-reduce constituent parser on the WSJ benchmark.- Anthology ID:
- D18-1161
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1303–1313
- Language:
- URL:
- https://aclanthology.org/D18-1161
- DOI:
- 10.18653/v1/D18-1161
- Cite (ACL):
- Daniel Fernández-González and Carlos Gómez-Rodríguez. 2018. Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1303–1313, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing (Fernández-González & Gómez-Rodríguez, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D18-1161.pdf
- Code
- danifg/Dynamic-InOrderParser
- Data
- Penn Treebank