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.

Authors
BibTeX

@inproceedings{huang2021,
    title = {A Globally Normalized Neural Model for Semantic Parsing},
    author = {Chenyang Huang and Wei Yang and Yanshuai Cao and Osmar Za{\"i}ane and Lili Mou},
    booktitle = {ACL-IJCNLP-2021 5th Workshop on Structured Prediction for NLP},
    month = jul,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
}