Abstract
Sequence-to-sequence constituent parsing requires a linearization to represent trees as sequences. Top-down tree linearizations, which can be based on brackets or shift-reduce actions, have achieved the best accuracy to date. In this paper, we show that these results can be improved by using an in-order linearization instead. Based on this observation, we implement an enriched in-order shift-reduce linearization inspired by Vinyals et al. (2015)’s approach, achieving the best accuracy to date on the English PTB dataset among fully-supervised single-model sequence-to-sequence constituent parsers. Finally, we apply deterministic attention mechanisms to match the speed of state-of-the-art transition-based parsers, thus showing that sequence-to-sequence models can match them, not only in accuracy, but also in speed.- Anthology ID:
- 2020.acl-main.376
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4092–4099
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.376
- DOI:
- 10.18653/v1/2020.acl-main.376
- Cite (ACL):
- Daniel Fernández-González and Carlos Gómez-Rodríguez. 2020. Enriched In-Order Linearization for Faster Sequence-to-Sequence Constituent Parsing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4092–4099, Online. Association for Computational Linguistics.
- Cite (Informal):
- Enriched In-Order Linearization for Faster Sequence-to-Sequence Constituent Parsing (Fernández-González & Gómez-Rodríguez, ACL 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.acl-main.376.pdf