Abstract
We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms. By using efficient, nearly arc-factored inference and a bidirectional-LSTM composed with a multi-layer perceptron, our base system is able to significantly improve the state of the art for semantic dependency parsing, without using hand-engineered features or syntax. We then explore two multitask learning approaches—one that shares parameters across formalisms, and one that uses higher-order structures to predict the graphs jointly. We find that both approaches improve performance across formalisms on average, achieving a new state of the art. Our code is open-source and available at https://github.com/Noahs-ARK/NeurboParser.- Anthology ID:
- P17-1186
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2037–2048
- Language:
- URL:
- https://aclanthology.org/P17-1186
- DOI:
- 10.18653/v1/P17-1186
- Cite (ACL):
- Hao Peng, Sam Thomson, and Noah A. Smith. 2017. Deep Multitask Learning for Semantic Dependency Parsing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2037–2048, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Deep Multitask Learning for Semantic Dependency Parsing (Peng et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/P17-1186.pdf
- Code
- Noahs-ARK/NeurboParser