Abstract
Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level information (such as variable names and arguments) is glossed over. Then, we fill in missing details by taking into account the natural language input and the sketch itself. Experimental results on four datasets characteristic of different domains and meaning representations show that our approach consistently improves performance, achieving competitive results despite the use of relatively simple decoders.- Anthology ID:
- P18-1068
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 731–742
- Language:
- URL:
- https://aclanthology.org/P18-1068
- DOI:
- 10.18653/v1/P18-1068
- Cite (ACL):
- Li Dong and Mirella Lapata. 2018. Coarse-to-Fine Decoding for Neural Semantic Parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 731–742, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Coarse-to-Fine Decoding for Neural Semantic Parsing (Dong & Lapata, ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P18-1068.pdf
- Code
- donglixp/coarse2fine + additional community code