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
- Editors:
- Zornitsa Kozareva, Sujith Ravi, Andreas Vlachos, Priyanka Agrawal, André Martins
- Venue:
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2021.spnlp-1.7.pdf
- Data
- CoNaLa