Abstract
Hypertagging, or supertagging for surface realization, is the process of assigning lexical categories to nodes in an input semantic graph. Previous work has shown that hypertagging significantly increases realization speed and quality by reducing the search space of the realizer. Building on recent work using LSTMs to improve accuracy on supertagging for parsing, we develop an LSTM hypertagging method for OpenCCG, an open source NLP toolkit for CCG. Our results show significant improvements in both hypertagging accuracy and downstream realization performance.- Anthology ID:
- W18-6528
- Volume:
- Proceedings of the 11th International Conference on Natural Language Generation
- Month:
- November
- Year:
- 2018
- Address:
- Tilburg University, The Netherlands
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 210–220
- Language:
- URL:
- https://aclanthology.org/W18-6528
- DOI:
- 10.18653/v1/W18-6528
- Cite (ACL):
- Reid Fu and Michael White. 2018. LSTM Hypertagging. In Proceedings of the 11th International Conference on Natural Language Generation, pages 210–220, Tilburg University, The Netherlands. Association for Computational Linguistics.
- Cite (Informal):
- LSTM Hypertagging (Fu & White, INLG 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-6528.pdf