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.

翻译：在本文中,我们提出了一个无背景语法(CFG)语法解析的全球标准化模型。 我们的模型没有预测概率,而是预测每个步骤的真正价值,没有受到标签偏差问题的影响。 实验表明,我们的方法在小型数据集方面优于本地的标准化模型,但对于大型数据集来说并没有改善。