A Globally Normalized Neural Model for Semantic Parsing

Chenyang Huang, Wei Yang, Yanshuai Cao, Osmar Zaïane, Lili Mou


Abstract
In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing. Instead of predicting a probability, our model predicts a real-valued score at each step and does not suffer from the label bias problem. Experiments show that our approach outperforms locally normalized models on small datasets, but it does not yield improvement on a large dataset.
Anthology ID:
2021.spnlp-1.7
Volume:
Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | spnlp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
61–66
Language:
URL:
https://aclanthology.org/2021.spnlp-1.7
DOI:
10.18653/v1/2021.spnlp-1.7
Bibkey:
Cite (ACL):
Chenyang Huang, Wei Yang, Yanshuai Cao, Osmar Zaïane, and Lili Mou. 2021. A Globally Normalized Neural Model for Semantic Parsing. In Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021), pages 61–66, Online. Association for Computational Linguistics.
Cite (Informal):
A Globally Normalized Neural Model for Semantic Parsing (Huang et al., spnlp 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.spnlp-1.7.pdf
Data
CoNaLa