Abstract
Semantic parsers conventionally construct logical forms bottom-up in a fixed order, resulting in the generation of many extraneous partial logical forms. In this paper, we combine ideas from imitation learning and agenda-based parsing to train a semantic parser that searches partial logical forms in a more strategic order. Empirically, our parser reduces the number of constructed partial logical forms by an order of magnitude, and obtains a 6x-9x speedup over fixed-order parsing, while maintaining comparable accuracy.- Anthology ID:
- Q15-1039
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 3
- Month:
- Year:
- 2015
- Address:
- Cambridge, MA
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 545–558
- Language:
- URL:
- https://aclanthology.org/Q15-1039
- DOI:
- 10.1162/tacl_a_00157
- Cite (ACL):
- Jonathan Berant and Percy Liang. 2015. Imitation Learning of Agenda-based Semantic Parsers. Transactions of the Association for Computational Linguistics, 3:545–558.
- Cite (Informal):
- Imitation Learning of Agenda-based Semantic Parsers (Berant & Liang, TACL 2015)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/Q15-1039.pdf
- Code
- worksheets/0x8fdfad31