  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.
